Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis

NIN-LIKE PROTEIN3.2 inhibits repressor Aux/IAA14 expression and enhances root biomass in maize seedlings under low nitrogen

Plants generally enhance their root growth in the form of greater biomass and/or root length to boost nutrient uptake in response to short-term low nitrogen (LN). However, the underlying mechanisms of short-term LN-mediated root growth remain largely elusive. Our genome-wide association study, haplotype analysis, and phenotyping of transgenic plants showed that the crucial nitrate signaling component NIN-LIKE PROTEIN3.2 (ZmNLP3.2), a positive regulator of root biomass, is associated with natural variations in root biomass of maize (Zea mays L.) seedlings under LN. The monocot-specific gene AUXIN/INDOLE-3-ACETIC ACID14 (ZmAux/IAA14) exhibited opposite expression patterns to ZmNLP3.2 in ZmNLP3.2 knockout and overexpression lines, suggesting that ZmNLP3.2 hampers ZmAux/IAA14 transcription. Importantly, ZmAux/IAA14 knockout seedlings showed a greater root dry weight (RDW), whereas ZmAux/IAA14 overexpression reduced RDW under LN compared with wild-type plants, indicating that ZmAux/IAA14 negatively regulates the RDW of LN-grown seedlings. Moreover, in vitro and vivo assays indicated that AUXIN RESPONSE FACTOR19 (ZmARF19) binds to and transcriptionally activates ZmAux/IAA14, which was weakened by the ZmNLP3.2-ZmARF19 interaction. The zmnlp3.2 ZmAux/IAA14-OE seedlings exhibited further reduced RDW compared with ZmAux/IAA14 overexpression lines when subjected to LN treatment, corroborating the ZmNLP3.2-ZmAux/IAA14 interaction. Thus, our study reveals a ZmNLP3.2-ZmARF19-ZmAux/IAA14 module regulating root biomass in response to nitrogen limitation in maize.

GRABSEEDS: extraction of plant organ traits through image analysis

BACKGROUND

Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits.

RESULTS

We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting.

CONCLUSION

GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .

Genomic and phenotypic characterization of finger millet indicates a complex diversification history

Advances in sequencing technologies mean that insights into crop diversification can now be explored in crops beyond major staples. We use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the whole and sub-genome level. A set of 8778 SNPs and 13 agronomic traits was used to characterize a diverse panel of 423 landraces from Africa and Asia. Through principal component analysis (PCA) and discriminant analysis of principal components, four distinct groups of accessions were identified that coincided with the primary geographic regions of finger millet cultivation. Notably, East Africa, presumed to be the crop's origin, exhibited the lowest genetic diversity. The PCA of phenotypic data also revealed geographic differentiation, albeit with differing relationships among geographic areas than indicated with genomic data. Further exploration of the sub-genomes A and B using neighbor-joining trees revealed distinct features that provide supporting evidence for the complex evolutionary history of finger millet. Although genome-wide association study found only a limited number of significant marker-trait associations, a clustering approach based on the distribution of marker effects obtained from a ridge regression genomic model was employed to investigate trait complexity. This analysis uncovered two distinct clusters. Overall, the findings suggest that finger millet has undergone complex and context-specific diversification, indicative of a lengthy domestication history. These analyses provide insights for the future development of finger millet.

Genome-wide association analysis reveals genes controlling an antagonistic effect of biotic and osmotic stress on Arabidopsis thaliana growth

While the response of Arabidopsis thaliana to drought, herbivory or fungal infection has been well-examined, the consequences of exposure to a series of such (a)biotic stresses are not well studied. This work reports on the genetic mechanisms underlying the Arabidopsis response to single osmotic stress, and to combinatorial stress, either fungal infection using Botrytis cinerea or herbivory using Pieris rapae caterpillars followed by an osmotic stress treatment. Several small-effect genetic loci associated with rosette dry weight (DW), rosette water content (WC), and the projected rosette leaf area in response to combinatorial stress were mapped using univariate and multi-environment genome-wide association approaches. A single-nucleotide polymorphism (SNP) associated with DROUGHT-INDUCED 19 (DI19) was identified by both approaches, supporting its potential involvement in the response to combinatorial stress. Several SNPs were found to be in linkage disequilibrium with known stress-responsive genes such as PEROXIDASE 34 (PRX34), BASIC LEUCINE ZIPPER 25 (bZIP25), RESISTANCE METHYLATED GENE 1 (RMG1) and WHITE RUST RESISTANCE 4 (WRR4). An antagonistic effect between biotic and osmotic stress was found for prx34 and arf4 mutants, which suggests PRX34 and ARF4 play an important role in the response to the combinatorial stress.

Drought response in Arabidopsis displays synergistic coordination between stems and leaves

The synergy between drought-responsive traits across different organs is crucial in the whole-plant mechanism influencing drought resilience. These organ interactions, however, are poorly understood, limiting our understanding of drought response strategies at the whole-plant level. Therefore, we need more integrative studies, especially on herbaceous species that represent many important food crops but remain underexplored in their drought response. We investigated inflorescence stems and rosette leaves of six Arabidopsis thaliana genotypes with contrasting drought tolerance, and combined anatomical observations with hydraulic measurements and gene expression studies to assess differences in drought response. The soc1ful double mutant was the most drought-tolerant genotype based on its synergistic combination of low stomatal conductance, largest stomatal safety margin, more stable leaf water potential during non-watering, reduced transcript levels of drought stress marker genes, and reduced loss of chlorophyll content in leaves, in combination with stems showing the highest embolism resistance, most pronounced lignification, and thickest intervessel pit membranes. In contrast, the most sensitive Cvi ecotype shows the opposite extreme of the same set of traits. The remaining four genotypes show variations in this drought syndrome. Our results reveal that anatomical, ecophysiological, and molecular adaptations across organs are intertwined, and multiple (differentially combined) strategies can be applied to acquire a certain level of drought tolerance.

GWAS in tetraploid potato: identification and validation of SNP markers associated with glycoalkaloid content

Genome-wide association studies (GWAS) are a useful tool to unravel the genetic architecture of complex traits, but the results can be difficult to interpret. Population structure, genetic heterogeneity, and rare alleles easily result in false positive or false negative associations. This paper describes the analysis of a GWAS panel combined with three bi-parental mapping populations to validate GWAS results, using phenotypic data for steroidal glycoalkaloid (SGA) accumulation and the ratio (SGR) between the two major glycoalkaloids α-solanine and α-chaconine in potato tubers. SGAs are secondary metabolites in the Solanaceae family, functional as a defence against various pests and pathogens and in high quantities toxic for humans. With GWAS, we identified five quantitative trait loci (QTL) of which Sga1.1, Sgr8.1, and Sga11.1 were validated, but not Sga3.1 and Sgr7.1. In the bi-parental populations, Sga5.1 and Sga7.1 were mapped, but these were not identified with GWAS. The QTLs Sga1.1, Sga7.1, Sgr7.1, and Sgr8.1 co-localize with genes GAME9, GAME 6/GAME 11, SGT1, and SGT2, respectively. For other genes involved in SGA synthesis, no QTLs were identified. The results of this study illustrate a number of pitfalls in GWAS of which population structure seems the most important. We also show that introgression breeding for disease resistance has introduced new haplotypes to the gene pool involved in higher SGA levels in certain pedigrees. Finally, we show that high SGA levels remain unpredictable in potato but that α-solanine/α-chaconine ratio has a predictable outcome with specific SGT1 and SGT2 haplotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01344-2.

Natural variation in root suberization is associated with local environment in Arabidopsis thaliana

Genetic signature of climate adaptation has been widely recognized across the genome of many organisms; however, the eco-physiological basis for linking genomic polymorphisms with local adaptations remains largely unexplored. Using a panel of 218 world-wide Arabidopsis accessions, we characterized the natural variation in root suberization by quantifying 16 suberin monomers. We explored the associations between suberization traits and 126 climate variables. We conducted genome-wide association analysis and integrated previous genotype-environment association (GEA) to identify the genetic bases underlying suberization variation and their involvements in climate adaptation. Root suberin content displays extensive variation across Arabidopsis populations and significantly correlates with local moisture gradients and soil characteristics. Specifically, enhanced suberization is associated with drier environments, higher soil cation-exchange capacity, and lower soil pH; higher proportional levels of very-long-chain suberin is negatively correlated with moisture availability, lower soil gravel content, and higher soil silt fraction. We identified 94 putative causal loci and experimentally proved that GPAT6 is involved in C16 suberin biosynthesis. Highly significant associations between the putative genes and environmental variables were observed. Roots appear highly responsive to environmental heterogeneity via regulation of suberization, especially the suberin composition. The patterns of suberization-environment correlation and the suberin-related GEA fit the expectations of local adaptation for the polygenic suberization trait.

cis-prenyltransferase 3 and α/β-hydrolase are new determinants of dolichol accumulation in Arabidopsis

Dolichols (Dols), ubiquitous components of living organisms, are indispensable for cell survival. In plants, as well as other eukaryotes, Dols are crucial for post-translational protein glycosylation, aberration of which leads to fatal metabolic disorders in humans and male sterility in plants. Until now, the mechanisms underlying Dol accumulation remain elusive. In this study, we have analysed the natural variation of the accumulation of Dols and six other isoprenoids among more than 120 Arabidopsis thaliana accessions. Subsequently, by combining QTL and GWAS approaches, we have identified several candidate genes involved in the accumulation of Dols, polyprenols, plastoquinone and phytosterols. The role of two genes implicated in the accumulation of major Dols in Arabidopsis-the AT2G17570 gene encoding a long searched for cis-prenyltransferase (CPT3) and the AT1G52460 gene encoding an α/β-hydrolase-is experimentally confirmed. These data will help to generate Dol-enriched plants which might serve as a remedy for Dol-deficiency in humans.

Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis

Dark-induced senescence provokes profound metabolic shifts to recycle nutrients and to guarantee plant survival. To date, research on these processes has largely focused on characterizing mutants deficient in individual pathways. Here, we adopted a time-resolved genome-wide association-based approach to characterize dark-induced senescence by evaluating the photochemical efficiency and content of primary and lipid metabolites at the beginning, or after 3 or 6 days in darkness. We discovered six patterns of metabolic shifts and identified 215 associations with 81 candidate genes being involved in this process. Among these associations, we validated the roles of four genes associated with glycine, galactinol, threonine, and ornithine levels. We also demonstrated the function of threonine and galactinol catabolism during dark-induced senescence. Intriguingly, we determined that the association between tyrosine contents and TYROSINE AMINOTRANSFERASE 1 influences enzyme activity of the encoded protein and transcriptional activity of the gene under normal and dark conditions, respectively. Moreover, the single-nucleotide polymorphisms affecting the expression of THREONINE ALDOLASE 1 and the amino acid transporter gene AVT1B, respectively, only underlie the variation in threonine and glycine levels in the dark. Taken together, these results allow us to present a very detailed model of the metabolic aspects of dark-induced senescence, as well as the process itself.

Complex evolution of novel red floral color in Petunia

Red flower color has arisen multiple times and is generally associated with hummingbird pollination. The majority of evolutionary transitions to red color proceeded from purple lineages and tend to be genetically simple, almost always involving a few loss-of-function mutations of major phenotypic effect. Here we report on the complex evolution of a novel red floral color in the hummingbird-pollinated Petunia exserta (Solanaceae) from a colorless ancestor. The presence of a red color is remarkable because the genus cannot synthesize red anthocyanins and P. exserta retains a nonfunctional copy of the key MYB transcription factor AN2. We show that moderate upregulation and a shift in tissue specificity of an AN2 paralog, DEEP PURPLE, restores anthocyanin biosynthesis in P. exserta. An essential shift in anthocyanin hydroxylation occurred through rebalancing the expression of three hydroxylating genes. Furthermore, the downregulation of an acyltransferase promotes reddish hues in typically purple pigments by preventing acyl group decoration of anthocyanins. This study presents a rare case of a genetically complex evolutionary transition toward the gain of a novel red color.

Naturally occurring circadian rhythm variation associated with clock gene loci in Swedish Arabidopsis accessions

Circadian clocks have evolved to resonate with external day and night cycles. However, these entrainment signals are not consistent everywhere and vary with latitude, climate and seasonality. This leads to divergent selection for clocks which are locally adapted. To investigate the genetic basis for this circadian variation, we used a delayed fluorescence imaging assay to screen 191 naturally occurring Swedish Arabidopsis accessions for their circadian phenotypes. We demonstrate that the period length co-varies with both geography and population sub-structure. Several candidate loci linked to period, phase and relative amplitude error (RAE) were revealed by genome-wide association mapping and candidate genes were investigated using TDNA mutants. We show that natural variation in a single non-synonymous substitution within COR28 is associated with a long-period and late-flowering phenotype similar to that seen in TDNA knock-out mutants. COR28 is a known coordinator of flowering time, freezing tolerance and the circadian clock; all of which may form selective pressure gradients across Sweden. We demonstrate the effect of the COR28-58S SNP in increasing period length through a co-segregation analysis. Finally, we show that period phenotypic tails remain diverged under lower temperatures and follow a distinctive "arrow-shaped" trend indicative of selection for a cold-biased temperature compensation response.

Natural variation in Arabidopsis thaliana rosette area unveils new genes involved in plant development

Growth is a complex trait influenced by multiple genes that act at different moments during the development of an organism. This makes it difficult to spot its underlying genetic mechanisms. Since plant growth is intimately related to the effective leaf surface area (ELSA), identifying genes controlling this trait will shed light on our understanding of plant growth. To find new genes with a significant contribution to plant growth, here we used the natural variation in Arabidopsis thaliana to perform a genome-wide association study of ELSA. To do this, the projected rosette area of 710 worldwide distributed natural accessions was measured and analyzed using the genome-wide efficient mixed model association algorithm. From this analysis, ten genes were identified having SNPs with a significant association with ELSA. To validate the implication of these genes into A. thaliana growth, six of them were further studied by phenotyping knock-out mutant plants. It was observed that rem1.2, orc1a, ppd1, and mcm4 mutants showed different degrees of reduction in rosette size, thus confirming the role of these genes in plant growth. Our study identified genes already known to be involved in plant growth but also assigned this role, for the first time, to other genes.

High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenne L.)

The natural genetic diversity of agricultural species is an essential genetic resource for breeding programs aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations (France, Belgium, Germany). Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species.

Recent Progress in Germplasm Evaluation and Gene Mapping to Enable Breeding of Drought-Tolerant Wheat

There is a need to increase wheat productivity to meet the food demands of the ever-growing human population. However, accelerated development of high yielding varieties is hindered by drought, which is worsening due to climate change. In this context, germplasm diversity is central to the development of drought-tolerant wheat. Extensive collections of these genetic resources are conserved in national and international genebanks. In addition to phenotypic assessments, the use of advanced molecular techniques (e.g., genotype by sequencing) to identify quantitative trait loci (QTLs) for drought tolerance related traits is useful for genome- and marker-assisted selection based approaches. Therefore, to assist wheat breeders at a critical time, we searched the recent peer-reviewed literature (2011-current), first, to identify wheat germplasm observed to be useful genetic sources for drought tolerance, and second, to report QTLs associated with drought tolerance. Though many breeders limit the parents used in breeding programs to a familiar core collection, the results of this review show that larger germplasm collections have been sources of useful genes for drought tolerance in wheat. The review also demonstrates that QTLs for drought tolerance in wheat are associated with diverse physio-morphological traits, at different growth stages. Here, we also briefly discuss the potential of genome engineering/editing to improve drought tolerance in wheat. The use of CRISPR-Cas9 and other gene-editing technologies can be used to fine-tune the expression of genes controlling drought adaptive traits, while high throughput phenotyping (HTP) techniques can potentially accelerate the selection process. These efforts are empowered by wheat researcher consortia.

Natural variation of photosynthetic efficiency in Arabidopsis thaliana accessions under low temperature conditions

Low, but non-freezing, temperatures have negative effects on plant growth and development. Despite some molecular signalling pathways being known, the mechanisms causing different responses among genotypes are still poorly understood. Photosynthesis is one of the processes that are affected by low temperatures. Using an automated phenotyping platform for chlorophyll fluorescence imaging the steady state quantum yield of photosystem II (PSII) electron transport (Φ_PSII ) was measured and used to quantify the effect of moderately low temperature on a population of Arabidopsis thaliana natural accessions. Observations were made over the course of several weeks in standard and low temperature conditions and a strong decrease in Φ_PSII upon the cold treatment was found. A genome wide association study identified several quantitative trait loci (QTLs) that are associated with changes in Φ_PSII in low temperature. One candidate for a cold specific QTL was validated with a mutant analysis to be one of the genes that is likely involved in the PSII response to the cold treatment. The gene encodes the PSII associated protein PSB27 which has already been implicated in the adaptation to fluctuating light.

The Root Foraging Response under Low Nitrogen Depends on DWARF1-Mediated Brassinosteroid Biosynthesis

Root developmental plasticity enables plants to adapt to limiting or fluctuating nutrient conditions in the soil. When grown under nitrogen (N) deficiency, plants develop a more exploratory root system by increasing primary and lateral root length. However, mechanisms underlying this so-called foraging response remain poorly understood. We performed a genome-wide association study in Arabidopsis (Arabidopsis thaliana) and we show here that noncoding variations of the brassinosteroid (BR) biosynthesis gene DWARF1 (DWF1) lead to variation of the DWF1 transcript level that contributes to natural variation of root elongation under low N. In addition to DWF1, other central BR biosynthesis genes upregulated under low N include CONSTITUTIVE PHOTOMORPHOGENIC DWARF, DWF4, and BRASSINOSTEROID-6-OXIDASE 2 Phenotypic characterization of knockout and knockdown mutants of these genes showed significant reduction of their root elongation response to low N, suggesting a systemic stimulation of BR biosynthesis to promote root elongation. Moreover, we show that low N-induced root elongation is associated with aboveground N content and that overexpression of DWF1 significantly improves plant growth and overall N accumulation. Our study reveals that mild N deficiency induces key genes in BR biosynthesis and that natural variation in BR synthesis contributes to the root foraging response, complementing the impact of enhanced BR signaling observed recently. Furthermore, these results suggest a considerable potential of BR biosynthesis to genetically engineer plants with improved N uptake.

Identification of QTLs involved in cold tolerance during the germination and bud stages of rice (Oryza sativa L.) via a high-density genetic map

Low-temperature tolerance during the germination and bud stages is an important characteristic of direct-seeded rice (DSR). Recombinant inbred lines (RILs) derived from indica rice H335, which is highly tolerant to low temperature, and indica rice CHA-1, which is sensitive to low temperature, were used to identify quantitative trait loci (QTLs) associated with low-temperature tolerance during the germination and bud stages. a total of 11 QTLs were detected based on a high-density genetic map; among these, six QTLs explained 5.13-9.42% of the total phenotypic variation explained (PVE) during the germination stage, and five QTLs explained 4.17-6.42% of the total PVE during the bud stage. All QTLs were distributed on chromosome 9, and all favourable alleles originated from H335. The physical position of each QTL was determined, and 11 QTLs were combined into five genetic loci; three of these loci are involved during the germination stage (loci 1, 2, and 3), and three are involved during the bud stage (loci 3, 4, and 5). Loci 2, 4 and 5 were repeatedly detected in the wet season (WS) and dry season (DS). Notably, loci 3 was detected during both the germination and bud stages. These loci are good candidates for future studies of gene function and could serve as highly valuable genetic factors for improving cold tolerance during the germination and bud stages of rice.

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle

Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3'/5' untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4-9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree.

Genome-Wide Association Study and Genomic Prediction Elucidate the Distinct Genetic Architecture of Aluminum and Proton Tolerance in Arabidopsis thaliana

Under acid soil conditions, Al stress and proton stress can occur, reducing root growth and function. However, these stressors are distinct, and tolerance to each is governed by multiple physiological processes. To better understand the genes that underlie these coincidental but experimentally separable stresses, a genome-wide association study (GWAS) and genomic prediction (GP) models were created for approximately 200 diverse Arabidopsis thaliana accessions. GWAS and genomic prediction identified 140/160 SNPs associated with Al and proton tolerance, respectively, which explained approximately 70% of the variance observed. Reverse genetics of the genes in loci identified novel Al and proton tolerance genes, including TON1-RECRUITING MOTIF 28 (AtTRM28) and THIOREDOXIN H-TYPE 1 (AtTRX1), as well as genes known to be associated with tolerance, such as the Al-activated malate transporter, AtALMT1. Additionally, variation in Al tolerance was partially explained by expression level polymorphisms of AtALMT1 and AtTRX1 caused by cis-regulatory allelic variation. These results suggest that we successfully identified the loci that regulate Al and proton tolerance. Furthermore, very small numbers of loci were shared by Al and proton tolerance as determined by the GWAS. There were substantial differences between the phenotype predicted by genomic prediction and the observed phenotype for Al tolerance. This suggested that the GWAS-undetectable genetic factors (e.g., rare-allele mutations) contributing to the variation of tolerance were more important for Al tolerance than for proton tolerance. This study provides important new insights into the genetic architecture that produces variation in the tolerance of acid soil.

Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness

The capacity of organisms to tune their development in response to environmental cues is pervasive in nature. This phenotypic plasticity is particularly striking in plants, enabled by their modular and continuous development. A good example is the activation of lateral shoot branches in Arabidopsis, which develop from axillary meristems at the base of leaves. The activity and elongation of lateral shoots depends on the integration of many signals both external (e.g. light, nutrient supply) and internal (e.g. the phytohormones auxin, strigolactone and cytokinin). Here, we characterise natural variation in plasticity of shoot branching in response to nitrate supply using two diverse panels of Arabidopsis lines. We find extensive variation in nitrate sensitivity across these lines, suggesting a genetic basis for variation in branching plasticity. High plasticity is associated with extreme branching phenotypes such that lines with the most branches on high nitrate have the fewest under nitrate deficient conditions. Conversely, low plasticity is associated with a constitutively moderate level of branching. Furthermore, variation in plasticity is associated with alternative life histories with the low plasticity lines flowering significantly earlier than high plasticity lines. In Arabidopsis, branching is highly correlated with fruit yield, and thus low plasticity lines produce more fruit than high plasticity lines under nitrate deficient conditions, whereas highly plastic lines produce more fruit under high nitrate conditions. Low and high plasticity, associated with early and late flowering respectively, can therefore be interpreted alternative escape vs mitigate strategies to low N environments. The genetic architecture of these traits appears to be highly complex, with only a small proportion of the estimated genetic variance detected in association mapping.

QTL and candidate genes associated with leaf anion concentrations in response to phosphate supply in Arabidopsis thaliana

BACKGROUND

Phosphorus is often present naturally in the soil as inorganic phosphate, Pi, which bio-availability is limited in many ecosystems due to low soil solubility and mobility. Plants respond to low Pi with a Pi Starvation Response, involving Pi sensing and long-distance signalling. There is extensive cross-talk between Pi homeostasis mechanisms and the homeostasis mechanism for other anions in response to Pi availability.

RESULTS

Recombinant Inbred Line (RIL) and Genome Wide Association (GWA) mapping populations, derived from or composed of natural accessions of Arabidopsis thaliana, were grown under sufficient and deficient Pi supply. Significant treatment effects were found for all traits and significant genotype x treatment interactions for the leaf Pi and sulphate concentrations. Using the RIL/QTL population, we identified 24 QTLs for leaf concentrations of Pi and other anions, including a major QTL for leaf sulphate concentration (SUL2) mapped to the bottom of chromosome (Chr) 1. GWA mapping found 188 SNPs to be associated with the measured traits, corresponding to 152 genes. One of these SNPs, associated with leaf Pi concentration, mapped to PP2A-1, a gene encoding an isoform of the catalytic subunit of a protein phosphatase 2A. Of two additional SNPs, associated with phosphate use efficiency (PUE), one mapped to AT5G49780, encoding a leucine-rich repeat protein kinase involved in signal transduction, and the other to SIZ1, a gene encoding a SUMO E3 ligase, and a known regulator of P starvation-dependent responses. One SNP associated with leaf sulphate concentration was found in SULTR2;1, encoding a sulphate transporter, known to enhance sulphate translocation from root to shoot under P deficiency. Finally, one SNP was mapped to FMO GS-OX4, a gene encoding glucosinolate S-oxygenase involved in glucosinolate biosynthesis, which located within the confidence interval of the SUL2 locus.

CONCLUSION

We identified several candidate genes with known functions related to anion homeostasis in response to Pi availability. Further molecular studies are needed to confirm and validate these candidate genes and understand their roles in examined traits. Such knowledge will contribute to future breeding for improved crop PUE .

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants

The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method's prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.

The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana

One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms

We report a computational approach (implemented in MS-DIAL 3.0; http://prime.psc.riken.jp/) for metabolite structure characterization using fully ¹³C-labeled and non-labeled plants and LC-MS/MS. Our approach facilitates carbon number determination and metabolite classification for unknown molecules. Applying our method to 31 tissues from 12 plant species, we assigned 1,092 structures and 344 formulae to 3,604 carbon-determined metabolite ions, 69 of which were found to represent structures currently not listed in metabolome databases.

Interactions of Tomato and Botrytis cinerea Genetic Diversity: Parsing the Contributions of Host Differentiation, Domestication, and Pathogen Variation

Although the impacts of crop domestication on specialist pathogens are well known, less is known about the interaction of crop variation and generalist pathogens. To study how genetic variation within a crop affects plant resistance to generalist pathogens, we infected a collection of wild and domesticated tomato accessions with a genetically diverse population of the generalist pathogen Botrytis cinerea We quantified variation in lesion size of 97 B. cinerea genotypes (isolates) on six domesticated tomato genotypes (Solanum lycopersicum) and six wild tomato genotypes (Solanum pimpinellifolium). Lesion size was significantly affected by large effects of the host and pathogen's genotype, with a much smaller contribution of domestication. This pathogen collection also enables genome-wide association mapping of B. cinerea Genome-wide association mapping of the pathogen showed that virulence is highly polygenic and involves a diversity of mechanisms. Breeding against this pathogen would likely require the use of diverse isolates to capture all possible mechanisms. Critically, we identified a subset of B. cinerea genes where allelic variation was linked to altered virulence against wild versus domesticated tomato, as well as loci that could handle both groups. This generalist pathogen already has a large collection of allelic variation that must be considered when designing a breeding program.

Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice

Seed germination and early seedling growth are important agricultural traits for developing populations of both irrigated and directly seeded rice (DSR). To investigate the genetic mechanisms underlying seed germination and early seedling growth in rice, 275 recombinant inbred lines (RILs) were genotyped in this study via the genotyping-by-sequencing (GBS) approach to construct a high-density linkage bin map based on the parent-independent genotyping method. Quantitative trait loci (QTLs) for 12 traits related to seed germination and early seedling growth were analyzed. Totally, 22 additive loci were detected, after analysis of the interaction between additive QTLs and environments, five stable additive loci were obtained. Among them, loci 4, 5, 12 and 14 exhibited clear pleiotropic effects that were associated with multiple traits. Analysis of the effects of the five additive stable loci showed that a single locus increased the corresponding phenotypic value. Ten of the 275 RILs pyramided the excellent alleles of the five stable genetic loci. Most phenotypic values of the ten RILs were greater than the average values. Four RILs (G260, G342, G371, and G401) with more excellent phenotypic values were subsequently selected; these RILs could serve as donor parents of favorable alleles in the breeding process. Due to the existence of pleiotropy, the use of these genetic loci for pyramid breeding can further increase the efficiency to reach breeding goals. In addition, these five stable loci have an average physical interval of only 170 kb, we also further identified five promising candidate genes by qRT-PCR, which provides us with a basis for future cloning of these genes. Overall, this work will help broaden our understanding of the genetic control of seed germination and early seedling growth, and this study provides both a good theoretical basis and a new genetic resource for the breeding of direct-seeded rice.

Understanding genetic control of root system architecture in soybean: Insights into the genetic basis of lateral root number

Developing crops with better root systems is a promising strategy to ensure productivity in both optimum and stress environments. Root system architectural traits in 397 soybean accessions were characterized and a high-density single nucleotide polymorphisms (SNPs)-based genome-wide association study was performed to identify the underlying genes associated with root structure. SNPs associated with root architectural traits specific to landraces and elite germplasm pools were detected. Four loci were detected in landraces for lateral root number (LRN) and distribution of root thickness in diameter Class I with a major locus on chromosome 16. This major loci was detected in the coding region of unknown protein, and subsequent analyses demonstrated that root traits are affected with mutated haplotypes of the gene. In elite germplasm pool, 3 significant SNPs in alanine-glyoxalate aminotransferase, Leucine-Rich Repeat receptor/No apical meristem, and unknown functional genes were found to govern multiple traits including root surface area and volume. However, no major loci were detected for LRN in elite germplasm. Nucleotide diversity analysis found evidence of selective sweeps around the landraces LRN gene. Soybean accessions with minor and mutated allelic variants of LRN gene were found to perform better in both water-limited and optimal field conditions.

Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits

Plant resistance to generalist pathogens with broad host ranges, such as Botrytis cinerea (Botrytis), is typically quantitative and highly polygenic. Recent studies have begun to elucidate the molecular genetic basis of plant-pathogen interactions using commonly measured traits, including lesion size and/or pathogen biomass. However, with the advent of digital imaging and high-throughput phenomics, there are a large number of additional traits available to study quantitative resistance. In this study, we used high-throughput digital imaging analysis to investigate previously poorly characterized visual traits of plant-pathogen interactions related to disease resistance using the Arabidopsis (Arabidopsis thaliana)/Botrytis pathosystem. From a large collection of visual lesion trait measurements, we focused on color, shape, and size to test how these aspects of the Arabidopsis/Botrytis interaction are genetically related. Through genome-wide association mapping in Arabidopsis, we show that lesion color and shape are genetically separable traits associated with plant disease resistance. Moreover, by employing defined mutants in 23 candidate genes identified from the genome-wide association mapping, we demonstrate links between loci and each of the different plant-pathogen interaction traits. These results expand our understanding of the functional mechanisms driving plant disease resistance.

Assessment of heterosis in two Arabidopsis thaliana common-reference mapping populations

Hybrid vigour, or heterosis, has been of tremendous importance in agriculture for the improvement of both crops and livestock. Notwithstanding large efforts to study the phenomenon of heterosis in the last decades, the identification of common molecular mechanisms underlying hybrid vigour remain rare. Here, we conducted a systematic survey of the degree of heterosis in Arabidopsis thaliana hybrids. For this purpose, two overlapping Arabidopsis hybrid populations were generated by crossing a large collection of naturally occurring accessions to two common reference lines. In these Arabidopsis hybrid populations the range of heterosis for several developmental and yield related traits was examined, and the relationship between them was studied. The traits under study were projected leaf area at 17 days after sowing, flowering time, height of the main inflorescence, number of side branches from the main stem or from the rosette base, total seed yield, seed weight, seed size and the estimated number of seeds per plant. Predominantly positive heterosis was observed for leaf area and height of the main inflorescence, whereas mainly negative heterosis was observed for rosette branching. For the other traits both positive and negative heterosis was observed in roughly equal amounts. For flowering time and seed size only low levels of heterosis were detected. In general the observed heterosis levels were highly trait specific. Furthermore, no correlation was observed between heterosis levels and the genetic distance between the parental lines. Since all selected lines were a part of the Arabidopsis genome wide association (GWA) mapping panel, a genetic mapping approach was applied to identify possible regions harbouring genetic factors causal for heterosis, with separate calculations for additive and dominance effects. Our study showed that the genetic mechanisms underlying heterosis were highly trait specific in our hybrid populations and greatly depended on the genetic background, confirming the elusive character of heterosis.

Quantitative epigenetics and evolution

Epigenetics refers to chemical modifications of chromatin or transcribed DNA that can influence gene activity and expression without changes in DNA sequence. The last 20 years have yielded breakthroughs in our understanding of epigenetic processes that impact many fields of biology. In this review, we discuss how epigenetics relates to quantitative genetics and evolution. We argue that epigenetics is important for quantitative genetics because: (1) quantitative genetics is increasingly being combined with genomics, and therefore we should expand our thinking to include cellular-level mechanisms that can account for phenotypic variance and heritability besides just those that are hard-coded in the DNA sequence; and (2) epigenetic mechanisms change how phenotypic variance is partitioned, and can thereby change the heritability of traits and how those traits are inherited. To explicate these points, we show that epigenetics can influence all aspects of the phenotypic variance formula: VP (total phenotypic variance) = VG (genetic variance) + VE (environmental variance) + VGxE (genotype-by-environment interaction) + 2COVGE (the genotype-environment covariance) + Vɛ (residual variance), requiring new strategies to account for different potential sources of epigenetic effects on phenotypic variance. We also demonstrate how each of the components of phenotypic variance not only can be influenced by epigenetics, but can also have evolutionary consequences. We argue that no sources of epigenetic effects on phenotypic variance can be easily cast aside in a quantitative genetic research program that seeks to understand evolutionary processes.

Genomic Prediction of Complex Phenotypes Using Genic Similarity Based Relatedness Matrix

In the last years, a series of methods for genomic prediction (GP) have been established, and the advantages of GP over pedigree best linear unbiased prediction (BLUP) have been reported. However, the majority of previously proposed GP models are purely based on mathematical considerations while seldom take the abundant biological knowledge into account. Prediction ability of those models largely depends on the consistency between the statistical assumptions and the underlying genetic architectures of traits of interest. In this study, gene annotation information was incorporated into GP models by constructing haplotypes with SNPs mapped to genic regions. Haplotype allele similarity between pairs of individuals was measured through different approaches at single gene level and then converted into whole genome level, which was then treated as a special kernel and used in kernel based GP models. Results shown that the gene annotation guided methods gave higher or at least comparable predictive ability in some traits, especially in the Arabidopsis dataset and the rice breeding population. Compared to SNP models and haplotype models without gene annotation, the gene annotation based models improved the predictive ability by 0.56~26.67% in the Arabidopsis and 1.62~16.53% in the rice breeding population, respectively. However, incorporating gene annotation slightly improved the predictive ability for several traits but did not show any extra gain for the rest traits in a chicken population. In conclusion, integrating gene annotation into GP models could be beneficial for some traits, species, and populations compared to SNP models and haplotype models without gene annotation. However, more studies are yet to be conducted to implicitly investigate the characteristics of these gene annotation guided models.

Genome-wide signatures of flowering adaptation to climate temperature: Regional analyses in a highly diverse native range of Arabidopsis thaliana

Current global change is fueling an interest to understand the genetic and molecular mechanisms of plant adaptation to climate. In particular, altered flowering time is a common strategy for escape from unfavourable climate temperature. In order to determine the genomic bases underlying flowering time adaptation to this climatic factor, we have systematically analysed a collection of 174 highly diverse Arabidopsis thaliana accessions from the Iberian Peninsula. Analyses of 1.88 million single nucleotide polymorphisms provide evidence for a spatially heterogeneous contribution of demographic and adaptive processes to geographic patterns of genetic variation. Mountains appear to be allele dispersal barriers, whereas the relationship between flowering time and temperature depended on the precise temperature range. Environmental genome-wide associations supported an overall genome adaptation to temperature, with 9.4% of the genes showing significant associations. Furthermore, phenotypic genome-wide associations provided a catalogue of candidate genes underlying flowering time variation. Finally, comparison of environmental and phenotypic genome-wide associations identified known (Twin Sister of FT, FRIGIDA-like 1, and Casein Kinase II Beta chain 1) and new (Epithiospecifer Modifier 1 and Voltage-Dependent Anion Channel 5) genes as candidates for adaptation to climate temperature by altered flowering time. Thus, this regional collection provides an excellent resource to address the spatial complexity of climate adaptation in annual plants.

Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program

Genomic prediction (GP) is now routinely performed in crop plants to predict unobserved phenotypes. The use of predicted phenotypes to make selections is an active area of research. Here, we evaluate GP for predicting grain yield and compare genomic and phenotypic selection by tracking lines advanced. We examined four independent nurseries of F3:6 and F3:7 lines trialed at 6 to 10 locations each year. Yield was analyzed using mixed models that accounted for experimental design and spatial variations. Genotype-by-sequencing provided nearly 27,000 high-quality SNPs. Average genomic predictive ability, estimated for each year by randomly masking lines as missing in steps of 10% from 10 to 90%, and using the remaining lines from the same year as well as lines from other years in a training set, ranged from 0.23 to 0.55. The predictive ability estimated for a new year using the other years ranged from 0.17 to 0.28. Further, we tracked lines advanced based on phenotype from each of the four F3:6 nurseries. Lines with both above average genomic estimated breeding value (GEBV) and phenotypic value (BLUP) were retained for more years compared to lines with either above average GEBV or BLUP alone. The number of lines selected for advancement was substantially greater when predictions were made with 50% of the lines from the testing year added to the training set. Hence, evaluation of only 50% of the lines yearly seems possible. This study provides insights to assess and integrate genomic selection in breeding programs of autogamous crops.

Genome-wide association mapping of the architecture of susceptibility to the root-knot nematode Meloidogyne incognita in Arabidopsis thaliana

Susceptibility to the root-knot nematode Meloidogyne incognita in plants is thought to be a complex trait based on multiple genes involved in cell differentiation, growth and defence. Previous genetic analyses of susceptibility to M. incognita have mainly focused on segregating dominant resistance genes in crops. It is not known if plants harbour significant genetic variation in susceptibility to M. incognita independent of dominant resistance. To study the genetic architecture of susceptibility to M. incognita, we analysed nematode reproduction on a highly diverse set of 340 natural inbred lines of Arabidopsis thaliana with genome-wide association mapping. We observed a surprisingly large variation in nematode reproduction among these lines. Genome-wide association mapping revealed four quantitative trait loci (QTLs) located on chromosomes 1 and 5 of A. thaliana significantly associated with reproductive success of M. incognita, none of which harbours typical resistance gene homologues. Mutant analysis of three genes located in two QTLs showed that the transcription factor BRASSINAZOLE RESISTANT1 and an F-box family protein may function as (co-)regulators of susceptibility to M. incognita in Arabidopsis. Our data suggest that breeding for loss-of-susceptibility, based on allelic variants critically involved in nematode feeding, could be used to make crops more resilient to root-knot nematodes.

Investigating Drought Tolerance in Chickpea Using Genome-Wide Association Mapping and Genomic Selection Based on Whole-Genome Resequencing Data

Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs.

Parental DNA Methylation States Are Associated with Heterosis in Epigenetic Hybrids

Despite the importance and wide exploitation of heterosis in commercial crop breeding, the molecular mechanisms behind this phenomenon are not completely understood. Recent studies have implicated changes in DNA methylation and small RNAs in hybrid performance; however, it remains unclear whether epigenetic changes are a cause or a consequence of heterosis. Here, we analyze a large panel of over 500 Arabidopsis (Arabidopsis thaliana) epigenetic hybrid plants (epiHybrids), which we derived from near-isogenic but epigenetically divergent parents. This proof-of-principle experimental system allowed us to quantify the contribution of parental methylation differences to heterosis. We measured traits such as leaf area, growth rate, flowering time, main stem branching, rosette branching, and final plant height and observed several strong positive and negative heterotic phenotypes among the epiHybrids. Using an epigenetic quantitative trait locus mapping approach, we were able to identify specific differentially methylated regions in the parental genomes that are associated with hybrid performance. Sequencing of methylomes, transcriptomes, and genomes of selected parent-epiHybrid combinations further showed that these parental differentially methylated regions most likely mediate the remodeling of methylation and transcriptional states at specific loci in the hybrids. Taken together, our data suggest that locus-specific epigenetic divergence between the parental lines can directly or indirectly trigger heterosis in Arabidopsis hybrids independent of genetic changes. These results add to a growing body of evidence that points to epigenetic factors as one of the key determinants of hybrid performance.

Natural variation reveals that OsSAP16 controls low-temperature germination in rice

Low temperature affects seed germination in plants, and low-temperature germination (LTG) is an important agronomic trait. Natural variation of LTG has been reported in rice, but the molecular basis for this variation is largely unknown. Here we report the phenotypic analysis of LTG in 187 rice natural accessions and a genome-wide association study (GWAS) of LTG in this collection. A total of 53 quantitative trait loci (QTLs) were found to be associated with LTG, of which 20 were located in previously reported QTLs. We further identified Stress-Associated Protein 16 (OsSAP16), coding for a zinc-finger domain protein, as a causal gene for one of the major LTG QTLs. Loss of OsSAP16 function reduces germination while greater expression of OsSAP16 enhances germination at low temperature. In addition, accessions with extremely high and low LTG values have correspondingly high and low OsSAP16 expression at low temperatures, suggesting that variation in expression of the OsSAP16 gene contributes to LTG variation. As the first case of identification of an LTG gene through GWAS, this study indicates that GWAS of natural accessions is an effective strategy in genetically dissecting LTG processes and gaining molecular understanding of low-temperature response and germination.

Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis

Central metabolism is a coordinated network that is regulated at multiple levels by resource availability and by environmental and developmental cues. Its genetic architecture has been investigated by mapping metabolite quantitative trait loci (QTL). A more direct approach is to identify enzyme activity QTL, which distinguishes between cis-QTL in structural genes encoding enzymes and regulatory trans-QTL. Using genome-wide association studies, we mapped QTL for 24 enzyme activities, nine metabolites, three structural components, and biomass in Arabidopsis thaliana We detected strong cis-QTL for five enzyme activities. A cis-QTL for UDP-glucose pyrophosphorylase activity in the UGP1 promoter is maintained through balancing selection. Variation in acid invertase activity reflects multiple evolutionary events in the promoter and coding region of VAC-INVcis-QTL were also detected for ADP-glucose pyrophosphorylase, fumarase, and phosphoglucose isomerase activity. We detected many trans-QTL, including transcription factors, E3 ligases, protein targeting components, and protein kinases, and validated some by knockout analysis. trans-QTL are more frequent but tend to have smaller individual effects than cis-QTL. We detected many colocalized QTL, including a multitrait QTL on chromosome 4 that affects six enzyme activities, three metabolites, protein, and biomass. These traits are coordinately modified by different ACCELERATED CELL DEATH6 alleles, revealing a trade-off between metabolism and defense against biotic stress.

A Comprehensive Image-based Phenomic Analysis Reveals the Complex Genetic Architecture of Shoot Growth Dynamics in Rice (Oryza sativa)

Early vigor is an important trait for many rice ( L.)-growing environments. However, genetic characterization and improvement for early vigor is hindered by the temporal nature of the trait and strong genotype × environment effects. We explored the genetic architecture of shoot growth dynamics during the early and active tillering stages by applying a functional modeling and genomewide association (GWAS) mapping approach on a diversity panel of ∼360 rice accessions. Multiple loci with small effects on shoot growth trajectory were identified, indicating a complex polygenic architecture. Natural variation for shoot growth dynamics was assessed in a subset of 31 accessions using RNA sequencing and hormone quantification. These analyses yielded a gibberellic acid (GA) catabolic gene, , which could influence GA levels to regulate vigor in the early tillering stage. Given the complex genetic architecture of shoot growth dynamics, the potential of genomic selection (GS) for improving early vigor was explored using all 36,901 single-nucleotide polymorphisms (SNPs) as well as several subsets of the most significant SNPs from GWAS. Shoot growth trajectories could be predicted with reasonable accuracy using the 50 most significant SNPs from GWAS (0.37-0.53); however, the accuracy of prediction was improved by including more markers, which indicates that GS may be an effective strategy for improving shoot growth dynamics during the vegetative growth stage. This study provides insights into the complex genetic architecture and molecular mechanisms underlying early shoot growth dynamics and provides a foundation for improving this complex trait in rice.

Classification of intra-specific variation in plant functional strategies reveals adaptation to climate

Background and Aims

In plants, extensive intra-specific variation exists in the allocation of resources between vegetative growth and reproduction, reflecting different functional strategies. A simple method for the classification of intra-specific variation in these strategies would enable characterization of evolutionary and ecological processes.

Methods

C-S-R theory can be applied to classify functional strategies (competitive C; stress tolerant, S; ruderal, R) in different plant species. Using a diverse set of arabidopsis ( Arabidopsis thaliana ) accessions grown under common conditions, it was tested whether a simple approach designed for allocating C-S-R strategies at the species level can also be used to analyse intra-specific variation.

Key Results

Substantial intra-specific variation between arabidopsis accessions was found along the S-R axis. There was a positive correlation of temperature at the geographical origin with the dimension of S and a negative correlation with the dimension of R. Flowering time in a natural annual cycle and leaf dry matter content were identified as the main determinants of this adaptation, with plants originating from warmer climates having a higher leaf dry matter content and flowering earlier in a common garden.

Conclusions

It was shown that functional strategies reflect adaptation to climate, with consequences for important traits such as fecundity and total plant dry weight. The approach could be used in genome-wide association studies to determine the genetic basis of functional strategies in wild species or crops.

A Guide to Genome-Wide Association Mapping in Plants

Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley & Sons, Inc.

Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping

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.

Genome-wide association analysis reveals distinct genetic architectures for single and combined stress responses in Arabidopsis thaliana

Plants are commonly exposed to abiotic and biotic stresses. We used 350 Arabidopsis thaliana accessions grown under controlled conditions. We employed genome-wide association analysis to investigate the genetic architecture and underlying loci involved in genetic variation in resistance to: two specialist insect herbivores, Pieris rapae and Plutella xylostella; and combinations of stresses, i.e. drought followed by P. rapae and infection by the fungal pathogen Botrytis cinerea followed by infestation by P. rapae. We found that genetic variation in resistance to combined stresses by drought plus P. rapae was limited compared with B. cinerea plus P. rapae or P. rapae alone. Resistance to the two caterpillars is controlled by different genetic components. There is limited overlap in the quantitative trait loci (QTLs) underlying resistance to combined stresses by drought plus P. rapae or B. cinerea plus P. rapae and P. rapae alone. Finally, several candidate genes involved in the biosynthesis of aliphatic glucosinolates and proteinase inhibitors were identified to be involved in resistance to P. rapae and P. xylostella, respectively. This study underlines the importance of investigating plant responses to combinations of stresses. The value of this approach for breeding plants for resistance to combinatorial stresses is discussed.

Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana

Plant primary metabolism is a highly coordinated, central, and complex network of biochemical processes regulated at both the genetic and post-translational levels. The genetic basis of this network can be explored by analyzing the metabolic composition of genetically diverse genotypes in a given plant species. Here, we report an integrative strategy combining quantitative genetic mapping and metabolite‒transcript correlation networks to identify functional associations between genes and primary metabolites in Arabidopsis thaliana. Genome-wide association study (GWAS) was used to identify metabolic quantitative trait loci (mQTL). Correlation networks built using metabolite and transcript data derived from a previously published time-course stress study yielded metabolite‒transcript correlations identified by covariation. Finally, results obtained in this study were compared with mQTL previously described. We applied a statistical framework to test and compare the performance of different single methods (network approach and quantitative genetics methods, representing the two orthogonal approaches combined in our strategy) with that of the combined strategy. We show that the combined strategy has improved performance manifested by increased sensitivity and accuracy. This combined strategy allowed the identification of 92 candidate associations between structural genes and primary metabolites, which not only included previously well-characterized gene‒metabolite associations, but also revealed novel associations. Using loss-of-function mutants, we validated two of the novel associations with genes involved in tyrosine degradation and in β-alanine metabolism. In conclusion, we demonstrate that applying our integrative strategy to the largely untapped resource of metabolite-transcript associations can facilitate the discovery of novel metabolite-related genes. This integrative strategy is not limited to A. thaliana, but generally applicable to other plant species.

Phosphate-Dependent Root System Architecture Responses to Salt Stress

Nutrient availability and salinity of the soil affect the growth and development of plant roots. Here, we describe how inorganic phosphate (Pi) availability affects the root system architecture (RSA) of Arabidopsis (Arabidopsis thaliana) and how Pi levels modulate responses of the root to salt stress. Pi starvation reduced main root length and increased the number of lateral roots of Arabidopsis Columbia-0 seedlings. In combination with salt, low Pi dampened the inhibiting effect of mild salt stress (75 mm) on all measured RSA components. At higher salt concentrations, the Pi deprivation response prevailed over the salt stress only for lateral root elongation. The Pi deprivation response of lateral roots appeared to be oppositely affected by abscisic acid signaling compared with the salt stress response. Natural variation in the response to the combination treatment of salt and Pi starvation within 330 Arabidopsis accessions could be grouped into four response patterns. When exposed to double stress, in general, lateral roots prioritized responses to salt, while the effect on main root traits was additive. Interestingly, these patterns were not identical for all accessions studied, and multiple strategies to integrate the signals from Pi deprivation and salinity were identified. By genome-wide association mapping, 12 genomic loci were identified as putative factors integrating responses to salt stress and Pi starvation. From our experiments, we conclude that Pi starvation interferes with salt responses mainly at the level of lateral roots and that large natural variation exists in the available genetic repertoire of accessions to handle the combination of stresses.

Rice OVERLY TOLERANT TO SALT 1 (OTS1) SUMO protease is a positive regulator of seed germination and root development

Salinity is one of the major environmental stresses affecting rice production worldwide. Improving rice salt tolerance is a critical step for sustainable food production. Posttranslational modifications of proteins greatly expand proteome diversity, increase functionality and allow quick responses to environmental stresses, all at low cost to the cell. SUMO mediated modification of substrate proteins is a highly dynamic process governed by the balance of activities of SUMO E3 ligases and deconjugating SUMO proteases. In recent years, SUMO (Small Ubiquitin like Modifier) conjugation of proteins has emerged as an influential regulator of stress signaling in the model plant Arabidopsis. However SUMOylation remain largely under studied in crop plants. We recently identified the SUMO protease gene family in rice and demonstrated a role for OsOTS1 SUMO proteases in salt stress. Interestingly, rice plants silencing OsOTS1 also show significantly reduced germination rate. Knockdown of OsOTS1 gene expression affects root growth by primarily reducing cell size rather than cell division.