Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel

ACCELERATED CELL DEATH 6 is a crucial genetic factor shaping the natural diversity of age- and salicylic acid-induced leaf senescence in Arabidopsis

Leaf senescence is a crucial process throughout evolution, vital for plant fitness as it facilitates the gradual shift of energy allocation between photosynthesis and catabolism overtime. This onset is influenced by a complex interplay of genetic and environmental factors, making senescence a key adaptation mechanism for plants in their natural habitats. Our study investigated the genetic mechanism underlying age-induced leaf senescence in Arabidopsis natural populations. Using a phenome high-throughput investigator, we comprehensively analyzed senescence responses across 234 Arabidopsis accessions and identified that environmental factors (e.g., ambient temperature) and physiological factors (e.g., defense responses) are substantially linked to senescence phenotypes. Through genome-wide association mapping, we identified the ACCELERATED CELL DEATH 6 (ACD6) locus as a potential regulator of senescence variation among natural accessions. Knocking out ACD6 in accessions with early and delayed senescence phenotypes resulted in varying degrees of delay in age-induced senescence, highlighting the accession-dependent regulatory role of ACD6 in leaf senescence. Furthermore, our findings suggest ACD6's involvement in senescence regulation via the salicylic acid signaling pathway. In summary, our study sheds light on the genetic regulation of leaf senescence in Arabidopsis natural populations, with the discovery of ACD6 as a potential candidate for genetic modification to enhance plant adaptation and survival.

Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits

We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidopsis Genomes Project. An existing body of evidence suggests that linear models may be impeded by their inability to make use of non-additive effects to explain phenotypic variation at the population level. The results presented here use a nested cross-validation approach to confirm that some machine learning methods have the ability to statistically outperform linear prediction models, with the optimal model dependent on availability of training data and genetic architecture of the trait in question. Linear models were competitive in their performance as per previous work, though the neural network class of predictors was observed to be the most accurate and robust for traits with high heritability. The extent to which non-linear models exploit interaction effects will require further investigation of the causal pathways that lay behind their predictions. Future work utilizing more traits and larger sample sizes, combined with an improved understanding of their respective genetic architectures, may lead to improvements in prediction accuracy.

The natural variance of Arabidopsis secondary metabolism on extended darkness

In plants due to their sessile nature, secondary metabolites are important components against different abiotic and biotic stress, such as extended darkness. For this reason, the variation of secondary metabolite content of the Arabidopsis thaliana HapMap natural population following 0-and 6-d darkness treatment were detected and the raw data of different accessions at two timepoints were deposited in the Zenodo database. Moreover, the annotated secondary metabolites of these samples are presented in this data descriptor, which we believe will be a usefully re-usable resource for future integrative analysis with dark-treated transcripts, proteins or other phenotypic data in order to comprehensively illustrate the multiomic landscape of Arabidopsis in response to the stresses exerted by extended darkness.

Nitrate transporter protein NPF5.12 and major latex-like protein MLP6 are important defense factors against Verticillium longisporum

Plant defense responses to the soil-borne fungus Verticillium longisporum causing stem stripe disease on oilseed rape (Brassica napus) are poorly understood. In this study, a population of recombinant inbred lines (RILs) using the Arabidopsis accessions Sei-0 and Can-0 was established. Composite interval mapping, transcriptome data, and T-DNA mutant screening identified the NITRATE/PEPTIDE TRANSPORTER FAMILY 5.12 (AtNPF5.12) gene as being associated with disease susceptibility in Can-0. Co-immunoprecipitation revealed interaction between AtNPF5.12 and the MAJOR LATEX PROTEIN family member AtMLP6, and fluorescence microscopy confirmed this interaction in the plasma membrane and endoplasmic reticulum. CRISPR/Cas9 technology was applied to mutate the NPF5.12 and MLP6 genes in B. napus. Elevated fungal growth in the npf5.12 mlp6 double mutant of both oilseed rape and Arabidopsis demonstrated the importance of these genes in defense against V. longisporum. Colonization of this fungus depends also on available nitrates in the host root. Accordingly, the negative effect of nitrate depletion on fungal growth was less pronounced in Atnpf5.12 plants with impaired nitrate transport. In addition, suberin staining revealed involvement of the NPF5.12 and MLP6 genes in suberin barrier formation. Together, these results demonstrate a dependency on multiple plant factors that leads to successful V. longisporum root infection.

Estimating scale-specific and localized spatial patterns in allele frequency

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

The great tit HapMap project: A continental-scale analysis of genomic variation in a songbird

A major aim of evolutionary biology is to understand why patterns of genomic diversity vary within taxa and space. Large-scale genomic studies of widespread species are useful for studying how environment and demography shape patterns of genomic divergence. Here, we describe one of the most geographically comprehensive surveys of genomic variation in a wild vertebrate to date; the great tit (Parus major) HapMap project. We screened ca 500,000 SNP markers across 647 individuals from 29 populations, spanning ~30 degrees of latitude and 40 degrees of longitude - almost the entire geographical range of the European subspecies. Genome-wide variation was consistent with a recent colonisation across Europe from a South-East European refugium, with bottlenecks and reduced genetic diversity in island populations. Differentiation across the genome was highly heterogeneous, with clear 'islands of differentiation', even among populations with very low levels of genome-wide differentiation. Low local recombination rates were a strong predictor of high local genomic differentiation (FST), especially in island and peripheral mainland populations, suggesting that the interplay between genetic drift and recombination causes highly heterogeneous differentiation landscapes. We also detected genomic outlier regions that were confined to one or more peripheral great tit populations, probably as a result of recent directional selection at the species' range edges. Haplotype-based measures of selection were related to recombination rate, albeit less strongly, and highlighted population-specific sweeps that likely resulted from positive selection. Our study highlights how comprehensive screens of genomic variation in wild organisms can provide unique insights into spatio-temporal evolutionary dynamics.

Natural genetic variation underlying the negative effect of elevated CO2 on ionome composition in Arabidopsis thaliana

The elevation of atmospheric CO2 leads to a decline in plant mineral content, which might pose a significant threat to food security in coming decades. Although few genes have been identified for the negative effect of elevated CO2 on plant mineral composition, several studies suggest the existence of genetic factors. Here, we performed a large-scale study to explore genetic diversity of plant ionome responses to elevated CO2, using six hundred Arabidopsis thaliana accessions, representing geographical distributions ranging from worldwide to regional and local environments. We show that growth under elevated CO2 leads to a global decrease of ionome content, whatever the geographic distribution of the population. We observed a high range of genetic diversity, ranging from the most negative effect to resilience or even to a benefit in response to elevated CO2. Using genome-wide association mapping, we identified a large set of genes associated with this response, and we demonstrated that the function of one of these genes is involved in the negative effect of elevated CO2 on plant mineral composition. This resource will contribute to understand the mechanisms underlying the effect of elevated CO2 on plant mineral nutrition, and could help towards the development of crops adapted to a high-CO2 world.

A pan-genome of 69 Arabidopsis thaliana accessions reveals a conserved genome structure throughout the global species range

Although originally primarily a system for functional biology, Arabidopsis thaliana has, owing to its broad geographical distribution and adaptation to diverse environments, developed into a powerful model in population genomics. Here we present chromosome-level genome assemblies of 69 accessions from a global species range. We found that genomic colinearity is very conserved, even among geographically and genetically distant accessions. Along chromosome arms, megabase-scale rearrangements are rare and typically present only in a single accession. This indicates that the karyotype is quasi-fixed and that rearrangements in chromosome arms are counter-selected. Centromeric regions display higher structural dynamics, and divergences in core centromeres account for most of the genome size variations. Pan-genome analyses uncovered 32,986 distinct gene families, 60% being present in all accessions and 40% appearing to be dispensable, including 18% private to a single accession, indicating unexplored genic diversity. These 69 new Arabidopsis thaliana genome assemblies will empower future genetic research.

Molecular mechanisms and regulation of recombination frequency and distribution in plants

KEY MESSAGE

Recent developments in understanding the distribution and distinctive features of recombination hotspots are reviewed and approaches are proposed to increase recombination frequency in coldspot regions. Recombination events during meiosis provide the foundation and premise for creating new varieties of crops. The frequency of recombination in different genomic regions differs across eukaryote species, with recombination generally occurring more frequently at the ends of chromosomes. In most crop species, recombination is rare in centromeric regions. If a desired gene variant is linked in repulsion with an undesired variant of a second gene in a region with a low recombination rate, obtaining a recombinant plant combining two favorable alleles will be challenging. Traditional crop breeding involves combining desirable genes from parental plants into offspring. Therefore, understanding the mechanisms of recombination and factors affecting the occurrence of meiotic recombination is important for crop breeding. Here, we review chromosome recombination types, recombination mechanisms, genes and proteins involved in the meiotic recombination process, recombination hotspots and their regulation systems and discuss how to increase recombination frequency in recombination coldspot regions.

Methylomes as key features for predicting recombination in some plant species

Knowing how chromosome recombination works is essential for plant breeding. It enables the design of crosses between different varieties to combine desirable traits and create new ones. This is because the meiotic crossovers between homologous chromatids are not purely random, and various strategies have been developed to describe and predict such exchange events. Recent studies have used methylation data to predict chromosomal recombination in rice using machine learning models. This approach proved successful due to the presence of a positive correlation between the CHH context cytosine methylation and recombination rates in rice chromosomes. This paper assesses the question if methylation can be used to predict recombination in four plant species: Arabidopsis, maize, sorghum, and tomato. The results indicate a positive association between CHH context methylation and recombination rates in certain plant species, with varying degrees of strength in their relationships. The CG and CHG methylation contexts show negative correlation with recombination. Methylation data was key effectively in predicting recombination in sorghum and tomato, with a mean determination coefficient of 0.65 ± 0.11 and 0.76 ± 0.05, respectively. In addition, the mean correlation values between predicted and experimental recombination rates were 0.83 ± 0.06 for sorghum and 0.90 ± 0.05 for tomato, confirming the significance of methylomes in both monocotyledonous and dicotyledonous species. The predictions for Arabidopsis and maize were not as accurate, likely due to the comparatively weaker relationships between methylation contexts and recombination, in contrast to sorghum and tomato, where stronger associations were observed. To enhance the accuracy of predictions, further evaluations using data sets closely related to each other might prove beneficial. In general, this methylome-based method holds great potential as a reliable strategy for predicting recombination rates in various plant species, offering valuable insights to breeders in their quest to develop novel and improved varieties.

Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

Genome-wide association study identifies a novel BMI1A QTL allele that confers FLC expression diversity in Arabidopsis thaliana

Identification and understanding of the genetic basis of natural variations in plants are essential for comprehending their phenotypic adaptation. Here, we report a genome-wide association study (GWAS) of FLOWERING LOCUS C (FLC) expression in 727 Arabidopsis accessions. We identified B LYMPHOMA MOLONEY MURINE LEUKEMIA VIRUS INSERTION REGION 1 HOMOLOG 1A (BMI1A) as a causal gene for one of the FLC expression quantitative trait loci (QTLs). Loss of function in BMI1A increases FLC expression and delays flowering time at 16 °C significantly compared with the wild type (Col-0). BMI1A activity is required for histone H3 lysine 27 trimethylation (H3K27me3) accumulation at the FLC, MADS AFFECTING FLOWERING 4 (MAF4), and MAF5 loci at low ambient temperature. We further uncovered two BMI1A haplotypes associated with the natural variation in FLC expression and flowering time at 16 °C, and demonstrated that polymorphisms in the BMI1A promoter region are the main contributor. Different BMI1A haplotypes are strongly associated with geographical distribution, and the low ambient temperature-sensitive BMI1A variants are associated with a lower mean temperature of the driest quarter of their collection sites compared with the temperature-non-responsive variants, indicating that the natural variations in BMI1A have adaptive functions in FLC expression and flowering time regulation. Therefore, our results provide new insights into the natural variations in FLC expression and flowering time diversity in plants.

Genome-wide association of dry (Tamar) date palm fruit color

Date palm (Phoenix dactylifera) fruit (dates) are an economically and culturally significant crop in the Middle East and North Africa. There are hundreds of different commercial cultivars producing dates with distinctive shapes, colors, and sizes. Genetic studies of some date palm traits have been performed, including sex determination, sugar content, and fresh fruit color. In this study, we used genome sequences and image data of 199 dry dates (Tamar) collected from 14 countries to identify genetic loci associated with the color of this fruit stage. Here, we find loci across multiple linkage groups (LG) associated with dry fruit color phenotype. We recover both the previously identified VIRESCENS (VIR) genotype associated with fresh fruit yellow or red color and new associations with the lightness and darkness of dry fruit. This study will add resolution to our understanding of date color phenotype, especially at the most commercially important Tamar stage.

Natural variation in Arabidopsis responses to Plasmodiophora brassicae reveals an essential role for Resistance to Plasmodiophora brasssicae 1 (RPB1)

Despite the identification of clubroot resistance genes in various Brassica crops our understanding of the genetic basis of immunity to Plasmodiophora brassicae infection in the model plant Arabidopsis thaliana remains limited. To address this issue, we performed a screen of 142 natural accessions and identified 11 clubroot-resistant Arabidopsis lines. Genome-wide association analysis identified several genetic loci significantly linked with resistance. Three genes from two of these loci were targeted for deletion by CRISPR/Cas9 mutation in resistant accessions Est-1 and Uod-1. Deletion of Resistance to Plasmodiophora brassicae 1 (RPB1) rendered both lines susceptible to the P. brassicae pathotype P1+. Further analysis of rpb1 knock-out Est-1 and Uod-1 lines showed that the RPB1 protein is required for activation of downstream defence responses, such as the expression of phytoalexin biosynthesis gene CYP71A13. RPB1 has recently been shown to encode a cation channel localised in the endoplasmic reticulum. The clubroot susceptible Arabidopsis accession Col-0 lacks a functional RPB1 gene; when Col-0 is transformed with RPB1 expression driven by its native promoter it is capable of activating RPB1 transcription in response to infection, but this is not sufficient to confer resistance. Transient expression of RPB1 in Nicotiana tabacum induced programmed cell death in leaves. We conclude that RPB1 is a critical component of the defence response to P. brassicae infection in Arabidopsis, acting downstream of pathogen recognition but required for the elaboration of effective resistance.

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

Indirect genetic effects are shaped by demographic history and ecology in Arabidopsis thaliana

The phenotype of an individual can be affected by the genes of its conspecifics through indirect genetic effects (IGEs). IGEs have been studied across different organisms including wild and domesticated animals and plants, but little is known about their genetic architecture. Here, in a large-scale intraspecific interaction experiment, we show that the contribution of IGEs to the biomass variation of Arabidopsis thaliana is comparable to values classically reported in animals. Moreover, we identify 11 loci explaining 85.1% of the variability in IGEs. We find that positive IGE alleles (that is, those with positive effects on neighbour biomass) occur both in relict accessions from southern Eurasia and in post-glacial colonizers from northern Scandinavia, and that they are likely to have two divergent origins: for nine loci, they evolved in the post-glacial colonizers independently from the relicts, while the two others were introgressed in the post-glacial colonizer from the relicts. Finally, we find that variation in IGEs probably reflects divergent adaptations to the contrasting environments of the edges and the centre of the native range of the species. These findings reveal a surprisingly tractable genetic basis of IGEs in A. thaliana that is shaped by the ecology and the demographic history of the species.

Predicting plasticity of rosette growth and metabolic fluxes in Arabidopsis thaliana

Plants can rapidly mitigate the effects of suboptimal growth environments by phenotypic plasticity of fitness-traits. While genetic variation for phenotypic plasticity offers the means for breeding climate-resilient crop lines, accurate genomic prediction models for plasticity of fitness-related traits are still lacking. Here, we employed condition- and accession-specific metabolic models for 67 Arabidopsis thaliana accessions to dissect and predict plasticity of rosette growth to changes in nitrogen availability. We showed that specific reactions in photorespiration, linking carbon and nitrogen metabolism, as well as key pathways of central carbon metabolism exhibited substantial genetic variation for flux plasticity. We also demonstrated that, in comparison with a genomic prediction model for fresh weight (FW), genomic prediction of growth plasticity improves the predictability of FW under low nitrogen by 58.9% and by additional 15.4% when further integrating data on plasticity of metabolic fluxes. Therefore, the combination of metabolic and statistical modeling provides a stepping stone in understanding the molecular mechanisms and improving the predictability of plasticity for fitness-related traits.

Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis

Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.

Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism

Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.

A Bayesian model for genomic prediction using metabolic networks

Motivation

Genomic prediction is now an essential technique in breeding and medicine, and it is interesting to see how omics data can be used to improve prediction accuracy. Precedent work proposed a metabolic network-based method in biomass prediction of Arabidopsis; however, the method consists of multiple steps that possibly degrade prediction accuracy.

Results

We proposed a Bayesian model that integrates all steps and jointly infers all fluxes of reactions related to biomass production. The proposed model showed higher accuracies than methods compared both in simulated and real data. The findings support the previous excellent idea that metabolic network information can be used for prediction.

Availability and implementation

All R and stan scripts to reproduce the results of this study are available at https://github.com/Onogi/MetabolicModeling.

Genome-wide association study of aphid abundance highlights a locus affecting plant growth and flowering in Arabidopsis thaliana

Plant life-history traits, such as size and flowering, contribute to shaping variation in herbivore abundance. Although plant genes involved in physical and chemical traits have been well studied, less is known about the loci linking plant life-history traits and herbivore abundance. Here, we conducted a genome-wide association study (GWAS) of aphid abundance in a field population of Arabidopsis thaliana. This GWAS of aphid abundance detected a relatively rare but significant variant on the third chromosome of A. thaliana, which was also suggestively but non-significantly associated with the presence or absence of inflorescence. Out of candidate genes near this significant variant, a mutant of a ribosomal gene (AT3G13882) exhibited slower growth and later flowering than a wild type under laboratory conditions. A no-choice assay with the turnip aphid, Lipaphis erysimi, found that aphids were unable to successfully establish on the mutant. Our GWAS of aphid abundance unexpectedly found a locus affecting plant growth and flowering.

QT-GWAS: A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

Although the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind, a large fraction of these metabolites and their biosynthetic pathways remain unknown. Resolving metabolite structures and their biosynthetic pathways is key to gaining biological understanding and to allow metabolic engineering. In order to retrieve novel biosynthetic genes involved in specialized metabolism, we developed a novel untargeted method designated as qualitative trait GWAS (QT-GWAS) that subjects qualitative metabolic traits to a genome-wide association study, while the conventional metabolite GWAS (mGWAS) mainly considers the quantitative variation of metabolites. As a proof of the validity of QT-GWAS, 23 and 15 of the retrieved associations identified in Arabidopsis thaliana by QT-GWAS and mGWAS, respectively, were supported by previous research. Furthermore, seven gene-metabolite associations retrieved by QT-GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme assays. As such, we established that CYTOCHROME P450 706A5 (CYP706A5) is involved in the biosynthesis of chroman derivatives, UDP-GLYCOSYLTRANSFERASE 76C3 (UGT76C3) is able to hexosylate guanine in vitro and in planta, and SULFOTRANSFERASE 202B1 (SULT202B1) catalyzes the sulfation of neolignans in vitro. Collectively, our study demonstrates that the untargeted QT-GWAS method can retrieve valid gene-metabolite associations at the level of enzyme-encoding genes, even new associations that cannot be found by the conventional mGWAS, providing a new approach for dissecting qualitative metabolic traits.

Pan-European study of genotypes and phenotypes in the Arabidopsis relative Cardamine hirsuta reveals how adaptation, demography, and development shape diversity patterns

We study natural DNA polymorphisms and associated phenotypes in the Arabidopsis relative Cardamine hirsuta. We observed strong genetic differentiation among several ancestry groups and broader distribution of Iberian relict strains in European C. hirsuta compared to Arabidopsis. We found synchronization between vegetative and reproductive development and a pervasive role for heterochronic pathways in shaping C. hirsuta natural variation. A single, fast-cycling ChFRIGIDA allele evolved adaptively allowing range expansion from glacial refugia, unlike Arabidopsis where multiple FRIGIDA haplotypes were involved. The Azores islands, where Arabidopsis is scarce, are a hotspot for C. hirsuta diversity. We identified a quantitative trait locus (QTL) in the heterochronic SPL9 transcription factor as a determinant of an Azorean morphotype. This QTL shows evidence for positive selection, and its distribution mirrors a climate gradient that broadly shaped the Azorean flora. Overall, we establish a framework to explore how the interplay of adaptation, demography, and development shaped diversity patterns of 2 related plant species.

Single-gene resolution of diversity-driven overyielding in plant genotype mixtures

In plant communities, diversity often increases productivity and functioning, but the specific underlying drivers are difficult to identify. Most ecological theories attribute positive diversity effects to complementary niches occupied by different species or genotypes. However, the specific nature of niche complementarity often remains unclear, including how it is expressed in terms of trait differences between plants. Here, we use a gene-centred approach to study positive diversity effects in mixtures of natural Arabidopsis thaliana genotypes. Using two orthogonal genetic mapping approaches, we find that between-plant allelic differences at the AtSUC8 locus are strongly associated with mixture overyielding. AtSUC8 encodes a proton-sucrose symporter and is expressed in root tissues. Genetic variation in AtSUC8 affects the biochemical activities of protein variants and natural variation at this locus is associated with different sensitivities of root growth to changes in substrate pH. We thus speculate that - in the particular case studied here - evolutionary divergence along an edaphic gradient resulted in the niche complementarity between genotypes that now drives overyielding in mixtures. Identifying genes important for ecosystem functioning may ultimately allow linking ecological processes to evolutionary drivers, help identify traits underlying positive diversity effects, and facilitate the development of high-performance crop variety mixtures.

Hello darkness, my old friend: 3-KETOACYL-COENZYME A SYNTHASE4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis thaliana

Plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness or extended darkness to a panel of ∼300 Arabidopsis (Arabidopsis thaliana) accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding an enzyme involved in very long chain fatty acid (VLCFA) synthesis, underlies the differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression of KCS4 in yeast and plants proved that KCS4 is a functional enzyme localized in the endoplasmic reticulum with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and leaf wax coverage, puTAG accumulation, and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlates with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation.

BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates.

RESULTS

We propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery control to select a set of candidate SNPs. After that, a model selection step searches through the space of LMMs that may have any number of SNPs from the candidate set. A simulation study shows that, when compared to popular GWAS procedures, BGWAS greatly reduces false positives while maintaining the same ability to detect true positive SNPs. We show the utility and flexibility of BGWAS with two case studies: a case study on salt stress in plants, and a case study on alcohol use disorder.

CONCLUSIONS

BGWAS maintains and in some cases increases the recall of true SNPs while drastically lowering the number of false positives compared to popular SMA procedures.

A VEL3 histone deacetylase complex establishes a maternal epigenetic state controlling progeny seed dormancy

Mother plants play an important role in the control of dormancy and dispersal characters of their progeny. In Arabidopsis seed dormancy is imposed by the embryo-surrounding tissues of the endosperm and seed coat. Here we show that VERNALIZATION5/VIN3-LIKE 3 (VEL3) maintains maternal control over progeny seed dormancy by establishing an epigenetic state in the central cell that primes the depth of primary seed dormancy later established during seed maturation. VEL3 colocalises with MSI1 in the nucleolus and associates with a histone deacetylase complex. Furthermore, VEL3 preferentially associates with pericentromeric chromatin and is required for deacetylation and H3K27me3 deposition established in the central cell. The epigenetic state established by maternal VEL3 is retained in mature seeds, and controls seed dormancy in part through repression of programmed cell death-associated gene ORE1. Our data demonstrates a mechanism by which maternal control of progeny seed physiology persists post-shedding, maintaining parental control of seed behaviour.

Natural variation of respiration-related traits in plants

Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.

Extensive sequence duplication in Arabidopsis revealed by pseudo-heterozygosity

BACKGROUND

It is apparent that genomes harbor much structural variation that is largely undetected for technical reasons. Such variation can cause artifacts when short-read sequencing data are mapped to a reference genome. Spurious SNPs may result from mapping of reads to unrecognized duplicated regions. Calling SNP using the raw reads of the 1001 Arabidopsis Genomes Project we identified 3.3 million (44%) heterozygous SNPs. Given that Arabidopsis thaliana (A. thaliana) is highly selfing, and that extensively heterozygous individuals have been removed, we hypothesize that these SNPs reflected cryptic copy number variation.

RESULTS

The heterozygosity we observe consists of particular SNPs being heterozygous across individuals in a manner that strongly suggests it reflects shared segregating duplications rather than random tracts of residual heterozygosity due to occasional outcrossing. Focusing on such pseudo-heterozygosity in annotated genes, we use genome-wide association to map the position of the duplicates. We identify 2500 putatively duplicated genes and validate them using de novo genome assemblies from six lines. Specific examples included an annotated gene and nearby transposon that transpose together. We also demonstrate that cryptic structural variation produces highly inaccurate estimates of DNA methylation polymorphism.

CONCLUSIONS

Our study confirms that most heterozygous SNP calls in A. thaliana are artifacts and suggest that great caution is needed when analyzing SNP data from short-read sequencing. The finding that 10% of annotated genes exhibit copy-number variation, and the realization that neither gene- nor transposon-annotation necessarily tells us what is actually mobile in the genome suggests that future analyses based on independently assembled genomes will be very informative.

Prediction of crossover recombination using parental genomes

Meiotic recombination is a crucial cellular process, being one of the major drivers of evolution and adaptation of species. In plant breeding, crossing is used to introduce genetic variation among individuals and populations. While different approaches to predict recombination rates for different species have been developed, they fail to estimate the outcome of crossings between two specific accessions. This paper builds on the hypothesis that chromosomal recombination correlates positively to a measure of sequence identity. It presents a model that uses sequence identity, combined with other features derived from a genome alignment (including the number of variants, inversions, absent bases, and CentO sequences) to predict local chromosomal recombination in rice. Model performance is validated in an inter-subspecific indica x japonica cross, using 212 recombinant inbred lines. Across chromosomes, an average correlation of about 0.8 between experimental and prediction rates is achieved. The proposed model, a characterization of the variation of the recombination rates along the chromosomes, can enable breeding programs to increase the chances of creating novel allele combinations and, more generally, to introduce new varieties with a collection of desirable traits. It can be part of a modern panel of tools that breeders can use to reduce costs and execution times of crossing experiments.

Genomic prediction in plants: opportunities for ensemble machine learning based approaches

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

Integrative genomic and transcriptomic analyses of a bud sport mutant 'Jinzao Wuhe' with the phenotype of large berries in grapevines

Background

Bud sport mutation occurs frequently in fruit plants and acts as an important approach for grapevine improvement and breeding. 'Jinzao Wuhe' is a bud sport of the elite cultivar 'Himord Seedless' with obviously enlarged organs and berries. To date, the molecular mechanisms underlying berry enlargement caused by bud sport in grapevines remain unclear.

Methods

Whole genome resequencing (WGRS) was performed for two pairs of bud sports and their maternal plants with similar phenotype to identify SNPs, InDels and structural variations (SVs) as well as related genes. Furthermore, transcriptomic sequencing at different developmental stages and weighted gene co-expression network analysis (WGCNA) for 'Jinzao Wuhe' and its maternal plant 'Himord Seedless' were carried out to identify the differentially expressed genes (DEGs), which were subsequently analyzed for Gene Ontology (GO) and function annotation.

Results

In two pairs of enlarged berry bud sports, a total of 1,334 SNPs, 272 InDels and 74 SVs, corresponding to 1,022 target genes related to symbiotic microorganisms, cell death and other processes were identified. Meanwhile, 1,149 DEGs associated with cell wall modification, stress-response and cell killing might be responsible for the phenotypic variation were also determined. As a result, 42 DEGs between 'Himord Seedless' and 'Jinzao Wuhe' harboring genetic variations were further investigated, including pectin esterase, cellulase A, cytochromes P450 (CYP), UDP-glycosyltransferase (UGT), zinc finger protein, auxin response factor (ARF), NAC transcription factor (TF), protein kinase, etc. These candidate genes offer important clues for a better understanding of developmental regulations of berry enlargement in grapevine.

Conclusion

Our results provide candidate genes and valuable information for dissecting the underlying mechanisms of berry development and contribute to future improvement of grapevine cultivars.

A genome-wide association study identifies novel players in Na and Fe homeostasis in Arabidopsis thaliana under alkaline-salinity stress

In nature, multiple stress factors occur simultaneously. The screening of natural diversity panels and subsequent Genome-Wide Association Studies (GWAS) is a powerful approach to identify genetic components of various stress responses. Here, the nutritional status variation of a set of 270 natural accessions of Arabidopsis thaliana grown on a natural saline-carbonated soil is evaluated. We report significant natural variation on leaf Na (LNa) and Fe (LFe) concentrations in the studied accessions. Allelic variation in the NINJA and YUC8 genes is associated with LNa diversity, and variation in the ALA3 is associated with LFe diversity. The allelic variation detected in these three genes leads to changes in their mRNA expression and correlates with plant differential growth performance when plants are exposed to alkaline salinity treatment under hydroponic conditions. We propose that YUC8 and NINJA expression patters regulate auxin and jasmonic signaling pathways affecting plant tolerance to alkaline salinity. Finally, we describe an impairment in growth and leaf Fe acquisition associated with differences in root expression of ALA3, encoding a phospholipid translocase active in plasma membrane and the trans Golgi network which directly interacts with proteins essential for the trafficking of PIN auxin transporters, reinforcing the role of phytohormonal processes in regulating ion homeostasis under alkaline salinity.

Increasing plant group productivity through latent genetic variation for cooperation

Historic yield advances in the major crops have, to a large extent, been achieved by selection for improved productivity of groups of plant individuals such as high-density stands. Research suggests that such improved group productivity depends on "cooperative" traits (e.g., erect leaves, short stems) that-while beneficial to the group-decrease individual fitness under competition. This poses a problem for some traditional breeding approaches, especially when selection occurs at the level of individuals, because "selfish" traits will be selected for and reduce yield in high-density monocultures. One approach, therefore, has been to select individuals based on ideotypes with traits expected to promote group productivity. However, this approach is limited to architectural and physiological traits whose effects on growth and competition are relatively easy to anticipate. Here, we developed a general and simple method for the discovery of alleles promoting cooperation in plant stands. Our method is based on the game-theoretical premise that alleles increasing cooperation benefit the monoculture group but are disadvantageous to the individual when facing noncooperative neighbors. Testing the approach using the model plant Arabidopsis thaliana, we found a major effect locus where the rarer allele was associated with increased cooperation and productivity in high-density stands. The allele likely affects a pleiotropic gene, since we find that it is also associated with reduced root competition but higher resistance against disease. Thus, even though cooperation is considered evolutionarily unstable except under special circumstances, conflicting selective forces acting on a pleiotropic gene might maintain latent genetic variation for cooperation in nature. Such variation, once identified in a crop, could rapidly be leveraged in modern breeding programs and provide efficient routes to increase yields.

Combined Approach of GWAS and Phylogenetic Analyses to Identify New Candidate Genes That Participate in Arabidopsis thaliana Primary Root Development Using Cellular Measurements and Primary Root Length

Genome-wide association studies (GWAS) have allowed the identification of different loci associated with primary root (PR) growth, and Arabidopsis is an excellent model for these studies. The PR length is controlled by cell proliferation, elongation, and differentiation; however, the specific contribution of proliferation and differentiation in the control of PR growth is still poorly studied. To this end, we analyzed 124 accessions and used a GWAS approach to identify potential causal genomic regions related to four traits: PR length, growth rate, cell proliferation and cell differentiation. Twenty-three genes and five statistically significant SNPs were identified. The SNP with the highest score mapped to the fifth exon of NAC048 and this change makes a missense variant in only 33.3% of the accessions with a large PR, compared with the accessions with a short PR length. Moreover, we detected five more SNPs in this gene and in NAC3 that allow us to discover closely related accessions according to the phylogenetic tree analysis. We also found that the association between genetic variants among the 18 genes with the highest scores in our GWAS and the phenotypic classes into which we divided our accessions are not straightforward and likely follow historical patterns.

BICOSS: Bayesian iterative conditional stochastic search for GWAS

Background

Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs).

Results

We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces false discovery rate and allows for smaller effect sizes to be discovered. Finally, two real world applications show the utility and flexibility of BICOSS.

Conclusions

When compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate.

Assessment of functional relevance of genes associated with local temperature variables in Arabidopsis thaliana

How likely genetic variations associated with environment identified in silico from genome wide association study are functionally relevant to environmental adaptation has been largely unexplored experimentally. Here we analyzed top 29 genes containing polymorphisms associated with local temperature variation (minimum, mean, maximum) among 1129 natural accessions of Arabidopsis thaliana. Their loss-of-function mutants were assessed for growth and stress tolerance at five temperatures. Twenty genes were found to affect growth or tolerance at one or more of these temperatures. Significantly, genes associated with maximum temperature more likely have a detect a function at higher temperature, while genes associated with minimum temperature more likely have a function at lower temperature. In addition, gene variants are distributed more frequently at geographic locations where they apparently offer an enhanced growth or tolerance for five genes tested. Furthermore, variations in a large proportion of the in silico identified genes associated with minimum or mean-temperatures exhibited a significant association with growth phenotypes experimentally assessed at low temperature for a small set of natural accessions. This study shows a functional relevance of gene variants associated with environmental variables and supports the feasibility of the use of local temperature factors in investigating the genetic basis of temperature adaptation.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Genome-wide association studies (GWAS) are commonly used in human and plant studies to identify genetic variants responsible for the phenotype of interest and provide foundations for studying disease mechanisms and crop improvement. Most GWAS models are developed and optimized using human datasets. However, the difference between human and plant datasets essentially limits their applications in plant studies, especially when mapping complex traits such as drought resistance and yield. In this study, we present a novel GWAS method, HapFM, tailored for plant datasets to overcome the difficulties of many conventional GWAS methods. HapFM resulted in higher statistical power than conventional GWAS methods for mapping complex traits in our simulation and real dataset analyses. In addition, HapFM reduced the mapping interval by prioritizing candidate causal regions in the genome, which benefits the downstream experimental studies. Last but not least, HapFM can incorporate biological annotations to increase statistical power further. Overall, HapFM balances statistical power, result interpretability, and downstream experimental verifiability.

DNA polymerase epsilon interacts with SUVH2/9 to repress the expression of genes associated with meiotic DSB hotspot in Arabidopsis

Meiotic recombination is initiated by the SPORULATION 11 (SPO11)-triggered formation of double-strand breaks (DSBs) that usually occur in open chromatin with active transcriptional features in many eukaryotes. However, gene transcription at DSB sites appears to be detrimental for repair, but the regulatory mechanisms governing transcription at meiotic DSB sites are largely undefined in plants. Here, we demonstrate that the largest DNA polymerase epsilon subunit POL2A interacts with SU(VAR)3 to 9 homologs SUVH2 and SUVH9. N-SIM (structured illumination microscopy) observation shows that the colocalization of SUVH2 with the meiotic DSB marker γ-H2AX is dependent on POL2A. RNA-seq of male meiocytes demonstrates that POL2A and SUVH2 jointly repress the expression of 865 genes, which have several known characteristics associated with meiotic DSB sites. Bisulfite-seq and small RNA-seq of male meiocytes support the idea that the silencing of these genes by POL2A and SUVH2/9 is likely independent of CHH methylation or 24-nt siRNA accumulation. Moreover, pol2a suvh2 suvh9 triple mutants have more severe defects in meiotic recombination and fertility compared with either pol2a or suvh2 suvh9. Our results not only identify a epigenetic regulatory mechanism for gene silencing in male meiocytes but also reveal roles for DNA polymerase and SUVH2/9 beyond their classic functions in mitosis.

Plant growth stimulation by high CO2 depends on phosphorus homeostasis in chloroplasts

Elevated atmospheric CO₂ enhances photosynthetic rate,¹ thereby increasing biomass production in plants. Nevertheless, high CO₂ reduces the accumulation of essential nutrients² such as phosphorus (P),³ which are required for photosynthetic processes and plant growth. How plants ensure enhanced growth despite meager P status remains enigmatic. In this study, we utilize genome-wide association analysis in Arabidopsis thaliana to identify a P transporter, PHT4;3, which mediates the reduction of P in chloroplasts at high CO₂. Decreasing chloroplastic P fine-tunes the accumulation of a sugar-P metabolite, phytic acid, to support plant growth. Furthermore, we demonstrate that this adaptive mechanism is conserved in rice. Our results establish a mechanistic framework for sustainable food production against the backdrop of soaring CO₂ levels across the world.

Phosphate transporter PHT1;1 is a key determinant of phosphorus acquisition in Arabidopsis natural accessions

Phosphorus (P) is a mineral nutrient essential for plant growth and development, but most P in the soil is unavailable for plants. To understand the genetic basis of P acquisition regulation, we performed genome-wide association studies (GWASs) on a diversity panel of Arabidopsis (Arabidopsis thaliana). Two primary determinants of P acquisition were considered, namely, phosphate (Pi)-uptake activity and PHOSPHATE TRANSPORTER 1 (PHT1) protein abundance. Association mapping revealed a shared significant peak on chromosome 5 (Chr5) where the PHT1;1/2/3 genes reside, suggesting a connection between the regulation of Pi-uptake activity and PHT1 protein abundance. Genes encoding transcription factors, kinases, and a metalloprotease associated with both traits were also identified. Conditional GWAS followed by statistical analysis of genotype-dependent PHT1;1 expression and transcriptional activity assays revealed an epistatic interaction between PHT1;1 and MYB DOMAIN PROTEIN 52 (MYB52) on Chr1. Further, analyses of F1 hybrids generated by crossing two subgroups of natural accessions carrying specific PHT1;1- and MYB52-associated single nucleotide polymorphisms (SNPs) revealed strong effects of these variants on PHT1;1 expression and Pi uptake activity. Notably, the soil P contents in Arabidopsis habitats coincided with PHT1;1 haplotype, emphasizing how fine-tuned P acquisition activity through natural variants allows environmental adaptation. This study sheds light on the complex regulation of P acquisition and offers a framework to systematically assess the effectiveness of GWAS approaches in the study of quantitative traits.

Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genetic heterogeneity and epistasis, would be expected to increase in severity with the geographical range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (less than 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than the previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Genomic prediction in plants: opportunities for ensemble machine learning based approaches

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

The megabase-scale crossover landscape is largely independent of sequence divergence

Meiotic recombination frequency varies along chromosomes and strongly correlates with sequence divergence. However, the causal relationship between recombination landscapes and polymorphisms is unclear. Here, we characterize the genome-wide recombination landscape in the quasi-absence of polymorphisms, using Arabidopsis thaliana homozygous inbred lines in which a few hundred genetic markers were introduced through mutagenesis. We find that megabase-scale recombination landscapes in inbred lines are strikingly similar to the recombination landscapes in hybrids, with the notable exception of heterozygous large rearrangements where recombination is prevented locally. In addition, the megabase-scale recombination landscape can be largely explained by chromatin features. Our results show that polymorphisms are not a major determinant of the shape of the megabase-scale recombination landscape but rather favour alternative models in which recombination and chromatin shape sequence divergence across the genome.

The frequency of recombination varies along chromosomes and highly correlates with sequence divergence. Here, the authors show that polymorphisms are not a major determinant of the megabase-scale recombination landscape in Arabidopsis, which is rather determined by chromatin accessibility and DNA methylation.

Knock-down of gene expression throughout meiosis and pollen formation by virus-induced gene silencing in Arabidopsis thaliana

Through the inactivation of genes that act during meiosis it is possible to direct the genetic make-up of plants in subsequent generations and optimize breeding schemes. Offspring may show higher recombination of parental alleles resulting from elevated crossover (CO) incidence, or by omission of meiotic divisions, offspring may become polyploid. However, stable mutations in genes essential for recombination, or for either one of the two meiotic divisions, can have pleiotropic effects on plant morphology and line stability, for instance by causing lower fertility. Therefore, it is often favorable to temporarily change gene expression during meiosis rather than relying on stable null mutants. It was previously shown that virus-induced gene silencing (VIGS) can be used to transiently reduce CO frequencies. We asked if VIGS could also be used to modify other processes throughout meiosis and during pollen formation in Arabidopsis thaliana. Here, we show that VIGS-mediated knock-down of FIGL1, RECQ4A/B, OSD1 and QRT2 can induce (i) an increase in chiasma numbers, (ii) unreduced gametes and (iii) pollen tetrads. We further show that VIGS can target both sexes and different genetic backgrounds and can simultaneously silence different gene copies. The successful knock-down of these genes in A. thaliana suggests that VIGS can be exploited to manipulate any process during or shortly after meiosis. Hence, the transient induction of changes in inheritance patterns can be used as a powerful tool for applied research and biotechnological applications.

Genome-Wide Association Study With Growth-Related Traits and Secondary Metabolite Contents in Red- and White-Heart Chinese Fir

Chinese fir [Cunninghamia lanceolata (Lamb.) Hook] is an important evergreen coniferous tree species that is widely distributed in many southern provinces of China and has important economic value. The Chinese fir accounts for 1/4 and 1/3 of the total artificial forest area and stock volume, respectively. Red-heart Chinese fir is popular in the market because of its high density and red heartwood. The long-growth cycle hindered the breeding process of Chinese fir, while molecular marker-assisted breeding could accelerate it. However, Chinese fir, a perennial conifer species, has a large genome, which has not yet been published. In this study, the growth-related traits and secondary metabolite contents of red- and white-heart Chinese fir were measured and found to be different between them. There are extremely significant differences among growth-related traits (p < 0.001), but secondary metabolite contents have different correlations due to differences in chemical structure. Moreover, genotype effect analysis of the substantially correlated single nucleotide polymorphisms (SNPs) revealed that most of the loci related to each growth-related traits were different from each other, indicating a type specificity of the genes regulated different growth-related traits. Furthermore, among the loci related to secondary metabolite contents, nine loci associated with multiple metabolite phenotypes such as Marker21022_4, Marker21022_172, Marker24559_31, Marker27425_37, Marker20748_85, Marker18841_115, Marker18841_198, Marker65846_146, and Marker21486_163, suggesting the presence of pleiotropic genes. This study identified the potential SNP markers associated with secondary metabolites in Chinese fir, thus setting the basis for molecular marker-assisted selection.

MAP KINASE PHOSPHATASE1 promotes osmotolerance by suppressing PHYTOALEXIN DEFICIENT4-independent immunity

Initial exposure of plants to osmotic stress caused by drought, cold, or salinity leads to acclimation, termed acquired tolerance, to subsequent severe stresses. Acquired osmotolerance induced by salt stress is widespread across Arabidopsis (Arabidopsis thaliana) accessions and is conferred by disruption of a nucleotide-binding leucine-rich repeat gene, designated ACQUIRED OSMOTOLERANCE. De-repression of this gene under osmotic stress causes detrimental autoimmunity via ENHANCED DISEASE SUSCEPTIBILITY1 and PHYTOALEXIN DEFICIENT4 (PAD4). However, the mechanism underlying acquired osmotolerance remains poorly understood. Here, we isolated an acquired osmotolerance-defective mutant (aod13) by screening 30,000 seedlings of an ion beam-mutagenized M2 population of Bu-5, an accession with acquired osmotolerance. We found that AOD13 encodes the dual-specificity phosphatase MAP KINASE PHOSPHATASE1 (MKP1), which negatively regulates MITOGEN-ACTIVATED PROTEIN KINASE3/6 (MPK3/6). Consistently, MPK3/6 activation was greater in aod13 than in the Bu-5 wild-type (WT). The aod13 mutant was sensitive to osmotic stress but tolerant to salt stress. Under osmotic stress, pathogenesis-related genes were strongly induced in aod13 but not in the Bu-5 WT. Loss of PAD4 in pad4 aod13 plants did not restore acquired osmotolerance, implying that activation of immunity independent of PAD4 renders aod13 sensitive to osmotic stress. These findings suggest that AOD13 (i.e. MKP1) promotes osmotolerance by suppressing the PAD4-independent immune response activated by MPK3/6.

Experimental demonstration and pan-structurome prediction of climate-associated riboSNitches in Arabidopsis

Background

Genome-wide association studies (GWAS) aim to correlate phenotypic changes with genotypic variation. Upon transcription, single nucleotide variants (SNVs) may alter mRNA structure, with potential impacts on transcript stability, macromolecular interactions, and translation. However, plant genomes have not been assessed for the presence of these structure-altering polymorphisms or “riboSNitches.”

Results

We experimentally demonstrate the presence of riboSNitches in transcripts of two Arabidopsis genes, ZINC RIBBON 3 (ZR3) and COTTON GOLGI-RELATED 3 (CGR3), which are associated with continentality and temperature variation in the natural environment. These riboSNitches are also associated with differences in the abundance of their respective transcripts, implying a role in regulating the gene's expression in adaptation to local climate conditions. We then computationally predict riboSNitches transcriptome-wide in mRNAs of 879 naturally inbred Arabidopsis accessions. We characterize correlations between SNPs/riboSNitches in these accessions and 434 climate descriptors of their local environments, suggesting a role of these variants in local adaptation. We integrate this information in CLIMtools V2.0 and provide a new web resource, T-CLIM, that reveals associations between transcript abundance variation and local environmental variation.

Conclusion

We functionally validate two plant riboSNitches and, for the first time, demonstrate riboSNitch conditionality dependent on temperature, coining the term “conditional riboSNitch.” We provide the first pan-genome-wide prediction of riboSNitches in plants. We expand our previous CLIMtools web resource with riboSNitch information and with 1868 additional Arabidopsis genomes and 269 additional climate conditions, which will greatly facilitate in silico studies of natural genetic variation, its phenotypic consequences, and its role in local adaptation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02656-4.

Apoplastic class III peroxidases PRX62 and PRX69 promote Arabidopsis root hair growth at low temperature

Root Hairs (RHs) growth is influenced by endogenous and by external environmental signals that coordinately regulate its final cell size. We have recently determined that RH growth was unexpectedly boosted when Arabidopsis thaliana seedlings are cultivated at low temperatures. It was proposed that RH growth plasticity in response to low temperature was linked to a reduced nutrient availability in the media. Here, we explore the molecular basis of this RH growth response by using a Genome Wide Association Study (GWAS) approach using Arabidopsis thaliana natural accessions. We identify the poorly characterized PEROXIDASE 62 (PRX62) and a related protein PRX69 as key proteins under moderate low temperature stress. Strikingly, a cell wall protein extensin (EXT) reporter reveals the effect of peroxidase activity on EXT cell wall association at 10 °C in the RH apical zone. Collectively, our results indicate that PRX62, and to a lesser extent PRX69, are key apoplastic PRXs that modulate ROS-homeostasis and cell wall EXT-insolubilization linked to RH elongation at low temperature.