Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana

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.

Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Landscape genomics reveals genetic signals of environmental adaptation of African wild eggplants

Crop wild relatives (CWR) provide a valuable resource for improving crops. They possess desirable traits that confer resilience to various environmental stresses. To fully utilize crop wild relatives in breeding and conservation programs, it is important to understand the genetic basis of their adaptation. Landscape genomics associates environments with genomic variation and allows for examining the genetic basis of adaptation. Our study examined the differences in allele frequency of 15,416 single nucleotide polymorphisms (SNPs) generated through genotyping by sequencing approach among 153 accessions of 15 wild eggplant relatives and two cultivated species from Africa, the principal hotspot of these wild relatives. We also explored the correlation between these variations and the bioclimatic and soil conditions at their collection sites, providing a comprehensive understanding of the genetic signals of environmental adaptation in African wild eggplant. Redundancy analysis (RDA) results showed that the environmental variation explained 6% while the geographical distances among the collection sites explained 15% of the genomic variation in the eggplant wild relative populations when controlling for population structure. Our findings indicate that even though environmental factors are not the main driver of selection in eggplant wild relatives, it is influential in shaping the genomic variation over time. The selected environmental variables and candidate SNPs effectively revealed grouping patterns according to the environmental characteristics of sampling sites. Using four genotype-environment association methods, we detected 396 candidate SNPs (2.5% of the initial SNPs) associated with eight environmental factors. Some of these SNPs signal genes involved in pathways that help adapt to environmental stresses such as drought, heat, cold, salinity, pests, and diseases. These candidate SNPs will be useful for marker-assisted improvement and characterizing the germplasm of this crop for developing climate-resilient eggplant varieties. The study provides a model for applying landscape genomics to other crops' wild relatives.

Flowering time: From physiology, through genetics to mechanism

Plant species have evolved different requirements for environmental/endogenous cues to induce flowering. Originally, these varying requirements were thought to reflect the action of different molecular mechanisms. Thinking changed when genetic and molecular analysis in Arabidopsis thaliana revealed that a network of environmental and endogenous signaling input pathways converge to regulate a common set of "floral pathway integrators." Variation in the predominance of the different input pathways within a network can generate the diversity of requirements observed in different species. Many genes identified by flowering time mutants were found to encode general developmental and gene regulators, with their targets having a specific flowering function. Studies of natural variation in flowering were more successful at identifying genes acting as nodes in the network central to adaptation and domestication. Attention has now turned to mechanistic dissection of flowering time gene function and how that has changed during adaptation. This will inform breeding strategies for climate-proof crops and help define which genes act as critical flowering nodes in many other species.

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.

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.

Overcoming the "feast or famine" effect: improved interaction testing in genome-wide association studies

In genetic association analysis of complex traits, detection of interaction (either GxG or GxE) can help to elucidate the genetic architecture and biological mechanisms underlying the trait. Detection of interaction in a genome-wide association study (GWAS) can be methodologically challenging for various reasons, including a high burden of multiple comparisons when testing for epistasis between all possible pairs of a set of genomewide variants, as well as heteroscedasticity effects occurring in the presence of GxG or GxE interaction. In this paper, we address the problem of an even more striking phenomenon that we call the "feast or famine" effect that occurs when testing interaction in a genomewide context. As we verify, even in a simplified setting in which there is no interaction at all (and so no heteroscedasticity), in a GWAS to detect GxG or GxE interaction with a fixed genetic variant or environmental factor, the distribution of the genome-wide p-values under the null hypothesis is not the i.i.d. uniform one that is commonly assumed. Using standard methods, even if all SNPs are independent, some GWASs will have systematically underinflated p-values ("feast"), and others will have systematically overinflated p-values ("famine"), which can lead to false detection of interaction, reduced power, inconsistent results across studies, and failure to replicate true signal. This startling phenomenon is specific to detection of interaction in a GWAS, and it may partly explain why such detection has so far proved challenging and difficult to replicate. We show theoretically that the key cause of this phenomenon is which variables are conditioned on in the analysis, and this suggests an approach to correct the problem by changing the way the conditioning is done. Using this insight, we have developed the TINGA method to adjust the interaction test statistics to make their p-values closer to uniform under the null hypothesis. In simulations we show that TINGA both controls type 1 error and improves power. TINGA allows for covariates and population structure through use of a linear mixed model and accounts for heteroscedasticity. We apply TINGA to detection of epistasis in a study of flowering time in Arabidopsis thaliana.

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.

Pervasive G × E interactions shape adaptive trajectories and the exploration of the phenotypic space in artificial selection experiments

Quantitative genetics models have shown that long-term selection responses depend on initial variance and mutational influx. Understanding limits of selection requires quantifying the role of mutational variance. However, correlative responses to selection on nonfocal traits can perturb the selection response on the focal trait; and generations are often confounded with selection environments so that genotype by environment (G×E) interactions are ignored. The Saclay divergent selection experiments (DSEs) on maize flowering time were used to track the fate of individual mutations combining genotyping data and phenotyping data from yearly measurements (DSEYM) and common garden experiments (DSECG) with four objectives: (1) to quantify the relative contribution of standing and mutational variance to the selection response, (2) to estimate genotypic mutation effects, (3) to study the impact of G×E interactions in the selection response, and (4) to analyze how trait correlations modulate the exploration of the phenotypic space. We validated experimentally the expected enrichment of fixed beneficial mutations with an average effect of +0.278 and +0.299 days to flowering, depending on the genetic background. Fixation of unfavorable mutations reached up to 25% of incoming mutations, a genetic load possibly due to antagonistic pleiotropy, whereby mutations fixed in the selection environment (DSEYM) turned to be unfavorable in the evaluation environment (DSECG). Global patterns of trait correlations were conserved across genetic backgrounds but exhibited temporal patterns. Traits weakly or uncorrelated with flowering time triggered stochastic exploration of the phenotypic space, owing to microenvironment-specific fixation of standing variants and pleiotropic mutational input.

Genetic adaptations in the population history of Arabidopsis thaliana

A population encounters a variety of environmental stresses, so the full source of its resilience can only be captured by collecting all the signatures of adaptation to the selection of the local environment in its population history. Based on the multiomic data of Arabidopsis thaliana, we constructed a database of phenotypic adaptations (p-adaptations) and gene expression (e-adaptations) adaptations in the population. Through the enrichment analysis of the identified adaptations, we inferred a likely scenario of adaptation that is consistent with the biological evidence from experimental work. We analyzed the dynamics of the allele frequencies at the 23,880 QTLs of 174 traits and 8,618 eQTLs of 1,829 genes with respect to the total SNPs in the genomes and identified 650 p-adaptations and 3,925 e-adaptations [false discovery rate (FDR) = 0.05]. The population underwent large-scale p-adaptations and e-adaptations along 4 lineages. Extremely cold winters and short summers prolonged seed dormancy and expanded the root system architecture. Low temperatures prolonged the growing season, and low light intensity required the increased chloroplast activity. The subtropical and humid environment enhanced phytohormone signaling pathways in response to the biotic and abiotic stresses. Exposure to heavy metals selected alleles for lower heavy metal uptake from soil, lower growth rate, lower resistance to bacteria, and higher expression of photosynthetic genes were selected. The p-adaptations are directly interpretable, while the coadapted gene expressions reflect the physiological requirements for the adaptation. The integration of this information characterizes when and where the population has experienced environmental stress and how the population responded at the molecular level.

Genome-wide association study unveils ascorbate regulation by PAS/LOV PROTEIN during high light acclimation

Varying light conditions elicit metabolic responses as part of acclimation with changes in ascorbate levels being an important component. Here, we adopted a genome-wide association-based approach to characterize the response in ascorbate levels on high light (HL) acclimation in a panel of 315 Arabidopsis (Arabidopsis thaliana) accessions. These studies revealed statistically significant SNPs for total and reduced ascorbate under HL conditions at a locus in chromosome 2. Ascorbate levels under HL and the region upstream and within PAS/LOV PROTEIN (PLP) were strongly associated. Intriguingly, subcellular localization analyses revealed that the PLPA and PLPB splice variants co-localized with VITAMIN C DEFECTIVE2 (VTC2) and VTC5 in both the cytosol and nucleus. Yeast 2-hybrid and bimolecular fluorescence complementation analyses revealed that PLPA and PLPB interact with VTC2 and that blue light diminishes this interaction. Furthermore, PLPB knockout mutants were characterized by 1.5- to 1.7-fold elevations in their ascorbate levels, whereas knockout mutants of the cry2 cryptochromes displayed 1.2- to 1.3-fold elevations compared to WT. Our results collectively indicate that PLP plays a critical role in the elevation of ascorbate levels, which is a signature response of HL acclimation. The results strongly suggest that this is achieved via the release of the inhibitory effect of PLP on VTC2 upon blue light illumination, as the VTC2-PLPB interaction is stronger under darkness. The conditional importance of the cryptochrome receptors under different environmental conditions suggests a complex hierarchy underpinning the environmental control of ascorbate levels. However, the data we present here clearly demonstrate that PLP dominates during HL acclimation.

Genetic Architecture of Flowering Time Differs Between Populations With Contrasting Demographic and Selective Histories

Understanding the evolutionary factors that impact the genetic architecture of traits is a central goal of evolutionary genetics. Here, we investigate how quantitative trait variation accumulated over time in populations that colonized a novel environment. We compare the genetic architecture of flowering time in Arabidopsis populations from the drought-prone Cape Verde Islands and their closest outgroup population from North Africa. We find that trait polygenicity is severely reduced in the island populations compared to the continental North African population. Further, trait architectures and reconstructed allelic histories best fit a model of strong directional selection in the islands in accord with a Fisher-Orr adaptive walk. Consistent with this, we find that large-effect variants that disrupt major flowering time genes (FRI and FLC) arose first, followed by smaller effect variants, including ATX2 L125F, which is associated with a 4-day reduction in flowering time. The most recently arising flowering time-associated loci are not known to be directly involved in flowering time, consistent with an omnigenic signature developing as the population approaches its trait optimum. Surprisingly, we find no effect in the natural population of EDI-Cvi-0 (CRY2 V367M), an allele for which an effect was previously validated by introgression into a Eurasian line. Instead, our results suggest the previously observed effect of the EDI-Cvi-0 allele on flowering time likely depends on genetic background, due to an epistatic interaction. Altogether, our results provide an empirical example of the effects demographic history and selection has on trait architecture.

INSurVeyor: improving insertion calling from short read sequencing data

Insertions are one of the major types of structural variations and are defined as the addition of 50 nucleotides or more into a DNA sequence. Several methods exist to detect insertions from next-generation sequencing short read data, but they generally have low sensitivity. Our contribution is two-fold. First, we introduce INSurVeyor, a fast, sensitive and precise method that detects insertions from next-generation sequencing paired-end data. Using publicly available benchmark datasets (both human and non-human), we show that INSurVeyor is not only more sensitive than any individual caller we tested, but also more sensitive than all of them combined. Furthermore, for most types of insertions, INSurVeyor is almost as sensitive as long reads callers. Second, we provide state-of-the-art catalogues of insertions for 1047 Arabidopsis Thaliana genomes from the 1001 Genomes Project and 3202 human genomes from the 1000 Genomes Project, both generated with INSurVeyor. We show that they are more complete and precise than existing resources, and important insertions are missed by existing methods.

Screening and validation of salt-stress responsive cg-SSR markers in wheat (Triticum aestivum L.) germplasm of Pakistan

BACKGROUND

Soil salinity has been affecting wheat production worldwide over past few decades. Evaluation of wheat genotypes for salinity tolerance at germination and vegetative growth level is crucial. Marker assisted selection is a technique used extensively for choosing salt-tolerant genotypes from breeding populations to introduce novel genes.

METHODS AND MATERIALS

The current study's main goal was to discover salt-stress resistant genes; genetic divergence and genome-wide connection by using recently designed candidate gene-based simple-sequence-repeat markers (cg-SSRs). The phenotypic connection of morphological features during the germination growth stage i.e., germination period, root length/weight and shoot length/weight, and vegetative growth stages i.e., root length/weight and shoot length/weight were tested in a group of 50 wheat genotypes. Significant difference was observed in germination rate, root length and weight among control and saline treatments.

CONCLUSION

Total 30 SSR markers were utilized to test salinity resistance genes in wheat genotypes. Three (10%) of which were monomorphic, one (3.34%) showed no result, and the other 26 (86%) were polymorphic. Using 30 polymorphic markers discovered total 37 alleles. The polymorphic information content (PIC), quantifies each SSR locus capacity to discriminate between wheat, varied from 0.00 to 0.38 with an average of 0.19. Association analysis revealed that 26 primers were associated with morphological features, 03 with root length and the remaining 23 with germination. Utilizing morphological data, stress tolerance index (STI) was designed concluding that Auqab-2000, Margala-99 and Ufaq showed better resistance against salinity among other wheat genotypes. Cluster analysis demonstrated that wheat genotypes have vast genetic variability.

Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and areas where new crops have been introduced, for example, for lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multienvironment dataset phenotyped for phenology-related traits across nine locations and over 3 yr along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions), which has also been genotyped with an exome capture array. Genome-wide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering-time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.

Epistatic interactions among multiple copies of FLC genes with naturally occurring insertions correlate with flowering time variation in radish

Brassicaceae crops, which underwent whole-genome triplication during their evolution, have multiple copies of flowering-related genes. Interactions among multiple gene copies may be involved in flowering time regulation; however, this mechanism is poorly understood. In this study, we performed comprehensive, high-throughput RNA sequencing analysis to identify candidate genes involved in the extremely late-bolting (LB) trait in radish. Then, we examined the regulatory roles and interactions of radish FLOWERING LOCUS C (RsFLC) paralogs, the main flowering repressor candidates. Seven flowering integrator genes, five vernalization genes, nine photoperiodic/circadian clock genes and eight genes from other flowering pathways were differentially expressed in the early-bolting (EB) cultivar 'Aokubinagafuto' and LB radish cultivar 'Tokinashi' under different vernalization conditions. In the LB cultivar, RsFLC1 and RsFLC2 expression levels were maintained after 40 days of cold exposure. Bolting time was significantly correlated with the expression rates of RsFLC1 and RsFLC2. Using the EB × LB F2 population, we performed association analyses of genotypes with or without 1910- and 1627-bp insertions in the first introns of RsFLC1 and RsFLC2, respectively. The insertion alleles prevented the repression of their respective FLC genes under cold conditions. Interestingly, genotypes homozygous for RsFLC2 insertion alleles maintained high RsFLC1 and RsFLC3 expression levels under cold conditions, and two-way analysis of variance revealed that RsFLC1 and RsFLC3 expression was influenced by the RsFLC2 genotype. Our results indicate that insertions in the first introns of RsFLC1 and RsFLC2 contribute to the late-flowering trait in radish via different mechanisms. The RsFLC2 insertion allele conferred a strong delay in bolting by inhibiting the repression of all three RsFLC genes, suggesting that radish flowering time is determined by epistatic interactions among multiple FLC gene copies.

Analysis of Arabidopsis non-reference accessions reveals high diversity of metabolic gene clusters and discovers new candidate cluster members

Metabolic gene clusters (MGCs) are groups of genes involved in a common biosynthetic pathway. They are frequently formed in dynamic chromosomal regions, which may lead to intraspecies variation and cause phenotypic diversity. We examined copy number variations (CNVs) in four Arabidopsis thaliana MGCs in over one thousand accessions with experimental and bioinformatic approaches. Tirucalladienol and marneral gene clusters showed little variation, and the latter was fixed in the population. Thalianol and especially arabidiol/baruol gene clusters displayed substantial diversity. The compact version of the thalianol gene cluster was predominant and more conserved than the noncontiguous version. In the arabidiol/baruol cluster, we found a large genomic insertion containing divergent duplicates of the CYP705A2 and BARS1 genes. The BARS1 paralog, which we named BARS2, encoded a novel oxidosqualene synthase. The expression of the entire arabidiol/baruol gene cluster was altered in the accessions with the duplication. Moreover, they presented different root growth dynamics and were associated with warmer climates compared to the reference-like accessions. In the entire genome, paired genes encoding terpene synthases and cytochrome P450 oxidases were more variable than their nonpaired counterparts. Our study highlights the role of dynamically evolving MGCs in plant adaptation and phenotypic diversity.

Genome-wide detection of genotype environment interactions for flowering time in Brassica napus

Flowering time is strongly related to the environment, while the genotype-by-environment interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 single nucleotide polymorphisms in 490 B. napus accessions were used to associate with the flowering time and related climatic index in eight environments using a compressed variance-component mixed model, 3VmrMLM. As a result, 19 stable main-effect quantitative trait nucleotides (QTNs) and 32 QTN-by-environment interactions (QEIs) for flowering time were detected. Four windows of daily average temperature and precipitation were found to be climatic factors highly correlated with flowering time. Ten main-effect QTNs were found to be associated with these flowering-time-related climatic indexes. Using differentially expressed gene (DEG) analysis in semi-winter and spring oilseed rapes, 5,850 and 5,511 DEGs were found to be significantly expressed before and after vernalization. Twelve and 14 DEGs, including 7 and 9 known homologs in Arabidopsis, were found to be candidate genes for stable QTNs and QEIs for flowering time, respectively. Five DEGs were found to be candidate genes for main-effect QTNs for flowering-time-related climatic index. These candidate genes, such as BnaFLCs, BnaFTs, BnaA02.VIN3, and BnaC09.PRR7, were further validated by the haplotype, selective sweep, and co-expression networks analysis. The candidate genes identified in this study will be helpful to breed B. napus varieties adapted to particular environments with optimized flowering time.

Testing the association of relative growth rate and adaptation to climate across natural ecotypes of Arabidopsis

Ecophysiologists have reported a range of relationships, including intrinsic trade-offs across and within species between plant relative growth rate in high resource conditions (RGR) vs adaptation to tolerate cold or arid climates, arising from trait-based mechanisms. Few studies have considered ecotypes within a species, in which the lack of a trade-off would contribute to a wide species range and resilience to climate change. For 15 ecotypes of Arabidopsis thaliana in a common garden we tested for associations between RGR vs adaptation to cold or dry native climates and assessed hypotheses for its mediation by 15 functional traits. Ecotypes native to warmer, drier climates had higher leaf density, leaf mass per area, root mass fraction, nitrogen per leaf area and carbon isotope ratio, and lower osmotic potential at full turgor. Relative growth rate was statistically independent of the climate of the ecotype native range and of individual functional traits. The decoupling of RGR and cold or drought adaptation in Arabidopsis is consistent with multiple stress resistance and avoidance mechanisms for ecotypic climate adaptation and would contribute to the species' wide geographic range and resilience as the climate changes.

Construction and analysis of a Noccaea caerulescens TILLING population

BACKGROUND

Metals such as Zn or Cd are toxic to plant and humans when they are exposed in high quantities through contaminated soil or food. Noccaea caerulescens, an extraordinary Zn/Cd/Ni hyperaccumulating species, is used as a model plant for metal hyperaccumulation and phytoremediation studies. Current reverse genetic techniques to generate mutants based on transgenesis is cumbersome due to the low transformation efficiency of this species. We aimed to establish a mutant library for functional genomics by a non-transgenic approach, to identify mutants with an altered mineral profiling, and to screen for mutations in bZIP19, a regulator of Zn homeostasis in N. caerulescens.

RESULTS

To generate the N. caerulescens mutant library, 3000 and 5000 seeds from two sister plants of a single-seed recurrent inbred descendant of the southern French accession Saint-Félix-de-Pallières (SF) were mutagenized respectively by 0.3 or 0.4% ethyl methane sulfonate (EMS). Two subpopulations of 5000 and 7000 M2 plants were obtained after 0.3 or 0.4% EMS treatment. The 0.4% EMS treatment population had a higher mutant frequency and was used for TILLING. A High Resolution Melting curve analysis (HRM) mutation screening platform was optimized and successfully applied to detect mutations for NcbZIP19, encoding a transcription factor controlling Zn homeostasis. Of four identified point mutations in NcbZIP19, two caused non-synonymous substitutions, however, these two mutations did not alter the ionome profile compared to the wild type. Forward screening of the 0.4% EMS treatment population by mineral concentration analysis (ionomics) in leaf material of each M2 plant revealed putative mutants affected in the concentration of one or more of the 20 trace elements tested. Several of the low-Zn mutants identified in the ionomic screen did not give progeny, illustrating the importance of Zn for the species. The mutant frequency of the population was evaluated based on an average of 2.3 knockout mutants per tested monogenic locus.

CONCLUSIONS

The 0.4% EMS treatment population is effectively mutagenized suitable for forward mutant screens and TILLING. Difficulties in seed production in low Zn mutants, obtained by both forward and reverse genetic approach, hampered further analysis of the nature of the low Zn phenotypes.

A natural uORF variant confers phosphorus acquisition diversity in soybean

Phosphorus (P) is an essential element for all organisms. Because P fertilizers are a non-renewable resource and high fixation in soils, sustainable agriculture requires researchers to improve crop P acquisition efficiency. Here, we report a strong association signal at a locus of CPU1 (component of phosphorus uptake 1), from a genome-wide association study of P acquisition efficiency in a soybean core collection grown in the field. A SEC12-like gene, GmPHF1, is identified as the causal gene for CPU1. GmPHF1 facilitates the ER (endoplasmic reticulum) exit of the phosphate transporter, GmPT4, to the plasma membrane of root epidermal cells. A common SNP in an upstream open reading frame (uORF) of GmPHF1, which alters the abundance of GmPHF1 in a tissue-specific manner, contributes to P acquisition diversity in soybean. A natural genetic variation conditions diversity in soybean P acquisition, which can be used to develop P-efficient soybean genotypes.

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.

GWAS on multiple traits identifies mitochondrial ACONITASE3 as important for acclimation to submergence stress

Flooding causes severe crop losses in many parts of the world. Genetic variation in flooding tolerance exists in many species; however, there are few examples for the identification of tolerance genes and their underlying function. We conducted a genome-wide association study (GWAS) in 387 Arabidopsis (Arabidopsis thaliana) accessions. Plants were subjected to prolonged submergence followed by desubmergence, and seven traits (score, water content, Fv/Fm, and concentrations of nitrate, chlorophyll, protein, and starch) were quantified to characterize their acclimation responses. These traits showed substantial variation across the range of accessions. A total of 35 highly significant single-nucleotide polymorphisms (SNPs) were identified across the 20 GWA datasets, pointing to 22 candidate genes, with functions in TCA cycle, DNA modification, and cell division. Detailed functional characterization of one candidate gene, ACONITASE3 (ACO3), was performed. Chromatin immunoprecipitation followed by sequencing showed that a single nucleotide polymorphism in the ACO3 promoter co-located with the binding site of the master regulator of retrograde signaling ANAC017, while subcellular localization of an ACO3-YFP fusion protein confirmed a mitochondrial localization during submergence. Analysis of mutant and overexpression lines determined changes in trait parameters that correlated with altered submergence tolerance and were consistent with the GWAS results. Subsequent RNA-seq experiments suggested that impairing ACO3 function increases the sensitivity to submergence by altering ethylene signaling, whereas ACO3 overexpression leads to tolerance by metabolic priming. These results indicate that ACO3 impacts submergence tolerance through integration of carbon and nitrogen metabolism via the mitochondrial TCA cycle and impacts stress signaling during acclimation to stress.

Preparation and Curation of Omics Data for Genome-Wide Association Studies

With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.

Opportunities and limits of controlled-environment plant phenotyping for climate response traits

Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.

Diverse phosphate and auxin transport loci distinguish phosphate tolerant from sensitive Arabidopsis accessions

Phosphorus (P) is an essential element for plant growth often limiting agroecosystems. To identify genetic determinants of performance under variable phosphate (Pi) supply, we conducted genome-wide association studies on five highly predictive Pi starvation response traits in 200 Arabidopsis (Arabidopsis thaliana) accessions. Pi concentration in Pi-limited organs had the strongest, and primary root length had the weakest genetic component. Of 70 trait-associated candidate genes, 17 responded to Pi withdrawal. The PHOSPHATE TRANSPORTER1 gene cluster on chromosome 5 comprises PHT1;1, PHT1;2, and PHT1;3 with known impact on P status. A second locus featured uncharacterized endomembrane-associated auxin efflux carrier encoding PIN-LIKES7 (PILS7) which was more strongly suppressed in Pi-limited roots of Pi-starvation sensitive accessions. In the Col-0 background, Pi uptake and organ growth were impaired in both Pi-limited pht1;1 and two pils7 T-DNA insertion mutants, while Pi -limited pht1;2 had higher biomass and pht1;3 was indistinguishable from wild-type. Copy number variation at the PHT1 locus with loss of the PHT1;3 gene and smaller scale deletions in PHT1;1 and PHT1;2 predicted to alter both protein structure and function suggest diversification of PHT1 is a key driver for adaptation to P limitation. Haplogroup analysis revealed a phosphorylation site in the protein encoded by the PILS7 allele from stress-sensitive accessions as well as additional auxin-responsive elements in the promoter of the "stress tolerant" allele. The former allele's inability to complement the pils7-1 mutant in the Col-0 background implies the presence of a kinase signaling loop controlling PILS7 activity in accessions from P-rich environments, while survival in P-poor environments requires fine-tuning of stress-responsive root auxin signaling.

From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data

In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.

Revisiting a GWAS peak in Arabidopsis thaliana reveals possible confounding by genetic heterogeneity

Genome-wide association studies (GWAS) have become a standard approach for exploring the genetic basis of phenotypic variation. However, correlation is not causation, and only a tiny fraction of all associations have been experimentally confirmed. One practical problem is that a peak of association does not always pinpoint a causal gene, but may instead be tagging multiple causal variants. In this study, we reanalyze a previously reported peak associated with flowering time traits in Swedish Arabidopsis thaliana population. The peak appeared to pinpoint the AOP2/AOP3 cluster of glucosinolate biosynthesis genes, which is known to be responsible for natural variation in herbivore resistance. Here we propose an alternative hypothesis, by demonstrating that the AOP2/AOP3 flowering association can be wholly accounted for by allelic variation in two flanking genes with clear roles in regulating flowering: NDX1, a regulator of the main flowering time controller FLC, and GA1, which plays a central role in gibberellin synthesis and is required for flowering under some conditions. In other words, we propose that the AOP2/AOP3 flowering-time association may be yet another example of a spurious, "synthetic" association, arising from trying to fit a single-locus model in the presence of two statistically associated causative loci. We conclude that caution is needed when using GWAS for fine-mapping.

Association between RsFT, RsFLC and RsCOL5 (A&B) expression and flowering regulation in Japanese wild radish

Flowering is an important step in the life cycle of plants and indicates adaptability to external climatic cues such as temperature and photoperiod. We investigated the expression patterns of core genes related to flowering-time regulation in Japanese wild radish (Raphanus sativus var. raphanistroides) with different vernalization requirements (obligate and facultative) and further identified climatic cues that may act as natural selective forces. Specifically, we analysed flowering-time variation under different cold and photoperiod treatments in Japanese wild radish collected from the Hokkaido (northern lineage) and Okinawa (southern lineage) islands, which experience contrasting climatic cues. The cultivation experiment verified the obligate and facultative vernalization requirements of the northern and southern wild radish accessions, respectively. The expression of major genes involved in flowering time indicated that RsFLC and RsCOL5 (A&B) may interact to regulate flowering time. Notably, floral initiation in the northern lineage was strongly correlated with RsFLC expression, whereas flowering in the southern linage was correlated with induction of RsCOL5-A expression, despite high RsFLC transcript levels. These results suggested that the northern accessions are more sensitive to prolonged cold exposure, whereas the southern accessions are more sensitive to photoperiod. These different mechanisms ultimately confer an optimal flowering time in natural populations in response to locally contrasting climatic cues. This study provides new insights into the variant mechanisms underlying floral pathways in Japanese wild radish from different geographic locations.

Genome-wide DNA mutations in Arabidopsis plants after multigenerational exposure to high temperatures

BACKGROUND

Elevated temperatures can cause physiological, biochemical, and molecular responses in plants that can greatly affect their growth and development. Mutations are the most fundamental force driving biological evolution. However, how long-term elevations in temperature influence the accumulation of mutations in plants remains unknown.

RESULTS

Multigenerational exposure of Arabidopsis MA (mutation accumulation) lines and MA populations to extreme heat and moderate warming results in significantly increased mutation rates in single-nucleotide variants (SNVs) and small indels. We observe distinctive mutational spectra under extreme and moderately elevated temperatures, with significant increases in transition and transversion frequencies. Mutation occurs more frequently in intergenic regions, coding regions, and transposable elements in plants grown under elevated temperatures. At elevated temperatures, more mutations accumulate in genes associated with defense responses, DNA repair, and signaling. Notably, the distribution patterns of mutations among all progeny differ between MA populations and MA lines, suggesting that stronger selection effects occurred in populations. Methylation is observed more frequently at mutation sites, indicating its contribution to the mutation process at elevated temperatures. Mutations occurring within the same genome under elevated temperatures are significantly biased toward low gene density regions, special trinucleotides, tandem repeats, and adjacent simple repeats. Additionally, mutations found in all progeny overlap significantly with genetic variations reported in 1001 Genomes, suggesting non-uniform distribution of de novo mutations through the genome.

CONCLUSION

Collectively, our results suggest that elevated temperatures can accelerate the accumulation, and alter the molecular profiles, of DNA mutations in plants, thus providing significant insight into how environmental temperatures fuel plant evolution.

Genome-wide association mapping for grain manganese in rice (Oryza sativa L.) using a multi-experiment approach

Manganese (Mn) is an essential trace element for plants and commonly contributes to human health; however, the understanding of the genes controlling natural variation in Mn in crop plants is limited. Here, the integration of two of genome-wide association study approaches was used to increase the identification of valuable quantitative trait loci (QTL) and candidate genes responsible for the concentration of grain Mn across 389 diverse rice cultivars grown in Arkansas and Texas, USA, in multiple years. Single-trait analysis was initially performed using three different SNP datasets. As a result, significant loci could be detected using the high-density SNP dataset. Based on the 5.2 M SNP dataset, major QTLs were located on chromosomes 3 and 7 for Mn containing six candidate genes. In addition, the phenotypic data of grain Mn concentration were combined from three flooded-field experiments from the two sites and 3 years using multi-experiment analysis based on the 5.2 M SNP dataset. Two previous QTLs on chromosome 3 were identified across experiments, whereas new Mn QTLs were identified that were not found in individual experiments, on chromosomes 3, 4, 9 and 11. OsMTP8.1 was identified in both approaches and is a good candidate gene that could be controlling grain Mn concentration. This work demonstrates the utilisation of multi-experiment analysis to identify constitutive QTLs and candidate genes associated with the grain Mn concentration. Hence, the approach should be advantageous to facilitate genomic breeding programmes in rice and other crops considering QTLs and genes associated with complex traits in natural populations.

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.

Molecular variation in a functionally divergent homolog of FCA regulates flowering time in Arabidopsis thaliana

The identification and functional characterization of natural variants in plants are essential for understanding phenotypic adaptation. Here we identify a molecular variation in At2g47310 that contributes to the natural variation in flowering time in Arabidopsis thaliana accessions. This gene, which we term SISTER of FCA (SSF), functions in an antagonistic manner to its close homolog FCA. Genome-wide association analysis screens two major haplotypes of SSF associated with the natural variation in FLC expression, and a single polymorphism, SSF-N414D, is identified as a main contributor. The SSF414N protein variant interacts more strongly with CUL1, a component of the E3 ubiquitination complex, than the SSF414D form, mediating differences in SSF protein degradation and FLC expression. FCA and SSF appear to have arisen through gene duplication after dicot-monocot divergence, with the SSF-N414D polymorphism emerging relatively recently within A. thaliana. This work provides a good example for deciphering the functional importance of natural polymorphisms in different organisms.

Natural variation represents valuable source for gene discovery. Here, the authors show that a homolog of Flowering Control Locus A (FCA) functions in an antagonistic manner to FCA in regulating Arabidopsis flowering time through interacting with CUL1-E3 and modulating FLC expression.

Using Local Convolutional Neural Networks for Genomic Prediction

The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. In this study, we analyze the use of artificial neural networks (ANN) and, in particular, local convolutional neural networks (LCNN) for genomic prediction, as a region-specific filter corresponds much better with our prior genetic knowledge on the genetic architecture of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000; p = 34,595) and real Arabidopsis data (n = 2,039; p = 180,000) for a variety of traits based on their predictive ability. The baseline LCNN, containing one local convolutional layer (kernel size: 10) and two fully connected layers with 64 nodes each, is outperforming commonly proposed ANNs (multi layer perceptrons and convolutional neural networks) for basically all considered traits. For traits with high heritability and large training population as present in the simulated data, LCNN are even outperforming state-of-the-art methods like genomic best linear unbiased prediction (GBLUP), Bayesian models and extended GBLUP, indicated by an increase in predictive ability of up to 24%. However, for small training populations, these state-of-the-art methods outperform all considered ANNs. Nevertheless, the LCNN still outperforms all other considered ANNs by around 10%. Minor improvements to the tested baseline network architecture of the LCNN were obtained by increasing the kernel size and of reducing the stride, whereas the number of subsequent fully connected layers and their node sizes had neglectable impact. Although gains in predictive ability were obtained for large scale data sets by using LCNNs, the practical use of ANNs comes with additional problems, such as the need of genotyping all considered individuals, the lack of estimation of heritability and reliability. Furthermore, breeding values are additive by design, whereas ANN-based estimates are not. However, ANNs also comes with new opportunities, as networks can easily be extended to account for additional inputs (omics, weather etc.) and outputs (multi-trait models), and computing time increases linearly with the number of individuals. With advances in high-throughput phenotyping and cheaper genotyping, ANNs can become a valid alternative for genomic prediction.

Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis

We study the genotype-phenotype maps of 80 quantitative phenotypes in the model plant Arabidopsis thaliana, by representing the genotypes affecting each phenotype as a genotype network. In such a network, each vertex or node corresponds to an individual's genotype at all those genomic loci that affect a given phenotype. Two vertices are connected by an edge if the associated genotypes differ in exactly one nucleotide. The 80 genotype networks we analyze are based on data from genome-wide association studies of 199 A. thaliana accessions. They form connected graphs whose topography differs substantially among phenotypes. We focus our analysis on the incidence of epistasis (non-additive interactions among mutations) because a high incidence of epistasis can reduce the accessibility of evolutionary paths towards high or low phenotypic values. We find epistatic interactions in 67 phenotypes, and in 51 phenotypes every pairwise mutant interaction is epistatic. Moreover, we find phenotype-specific differences in the fraction of accessible mutational paths to maximum phenotypic values. However, even though epistasis affects the accessibility of maximum phenotypic values, the relationships between genotypic and phenotypic change of our analyzed phenotypes are sufficiently smooth that some evolutionary paths remain accessible for most phenotypes, even where epistasis is pervasive. The genotype network representation we use can complement existing approaches to understand the genetic architecture of polygenic traits in many different organisms.

Genome-wide association study of turnip mosaic virus resistance in non-heading Chinese cabbage

A genome-wide association study (GWAS) using 83 diverse non-heading Chinese cabbage (NHCC) accessions identified 42,526 high-quality single nucleotide polymorphism markers associated with turnip mosaic virus (TuMV) resistance. Seventeen associated loci were identified, along with the related genes that were differentially expressed between resistant and susceptible varieties, suggesting that they may be candidate genes for TuMV tolerance. Nine mutant genes of Arabidopsis were selected for inoculation with TuMV-GFP (green fluorescence protein) to further confirm the disease resistance of these genes. Quantitative polymerase chain reaction (qPCR) analysis showed that the virus content in the Arabidopsis mutants with the homologous genes of cell wall-associated proteins, pectin methyl-esterase (PME), transcription factors (TFs), resistance gene (R), VAN3/SFC protein and F-box gene were significantly higher than that in the mutants with the homologous genes of methylation and J protein. Our results provide the basis of further study of the potential function of these candidate TuMV resistance genes and demonstrate that the described diverse NHCC can be efficiently used for GWAS of various quantitative traits. Taken together, the findings of this study will be useful to improve TuMV resistance in NHCC breeding and to discover new genes related to TuMV resistance.

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.

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.

Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa

Accurate manipulation of metabolites in monolignol biosynthesis is a key step for controlling lignin content, structure, and other wood properties important to the bioenergy and biomaterial industries. A crucial component of this strategy is predicting how single and combinatorial knockdowns of monolignol specific gene transcripts influence the abundance of monolignol proteins, which are the driving mechanisms of monolignol biosynthesis. Computational models have been developed to estimate protein abundances from transcript perturbations of monolignol specific genes. The accuracy of these models, however, is hindered by their inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences on transgenic transcript and protein abundances, identifying putative indirect regulatory influences that occur when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on targeted knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we showed that our model more accurately estimated transcript and protein abundances, in comparison to previous models, when individual and families of monolignol genes were perturbed. We leveraged insight from the inferred network structure obtained from our model to identify potential genes, including PtrHCT, PtrCAD, and Ptr4CL, involved in post-transcriptional and/or post-translational regulation. Our model provides a useful computational tool for exploring the cascaded impact of single and combinatorial modifications of monolignol specific genes on lignin and other wood properties.

Functional variants of DOG1 control seed chilling responses and variation in seasonal life-history strategies in Arabidopsis thaliana

The seasonal timing of seed germination is critical for plant fitness in different climates. To germinate at the right time of year, seeds respond to seasonal environmental cues, such as cold temperatures. We characterized genetic variation in seed dormancy responses to cold across the geographic range of a widespread annual plant. Induction of secondary seed dormancy during winter conditions (which restricts germination to autumn) was positively correlated with flowering time, constructing winter and spring seasonal life-history strategies. Variation in seed chilling responses was strongly associated with functional variants of a known dormancy gene. These variants showed evidence of ancient diversification associated with Pleistocene glacial cycles, and were associated with climate gradients across the species’ geographical range.

The seasonal timing of seed germination determines a plant’s realized environmental niche, and is important for adaptation to climate. The timing of seasonal germination depends on patterns of seed dormancy release or induction by cold and interacts with flowering-time variation to construct different seasonal life histories. To characterize the genetic basis and climatic associations of natural variation in seed chilling responses and associated life-history syndromes, we selected 559 fully sequenced accessions of the model annual species Arabidopsis thaliana from across a wide climate range and scored each for seed germination across a range of 13 cold stratification treatments, as well as the timing of flowering and senescence. Germination strategies varied continuously along 2 major axes: 1) Overall germination fraction and 2) induction vs. release of dormancy by cold. Natural variation in seed responses to chilling was correlated with flowering time and senescence to create a range of seasonal life-history syndromes. Genome-wide association identified several loci associated with natural variation in seed chilling responses, including a known functional polymorphism in the self-binding domain of the candidate gene DOG1. A phylogeny of DOG1 haplotypes revealed ancient divergence of these functional variants associated with periods of Pleistocene climate change, and Gradient Forest analysis showed that allele turnover of candidate SNPs was significantly associated with climate gradients. These results provide evidence that A. thaliana’s germination niche and correlated life-history syndromes are shaped by past climate cycles, as well as local adaptation to contemporary climate.

AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana

Genome-wide association studies (GWAS) are integral for studying genotype-phenotype relationships and gaining a deeper understanding of the genetic architecture underlying trait variation. A plethora of genetic associations between distinct loci and various traits have been successfully discovered and published for the model plant Arabidopsis thaliana. This success and the free availability of full genomes and phenotypic data for more than 1,000 different natural inbred lines led to the development of several data repositories. AraPheno (https://arapheno.1001genomes.org) serves as a central repository of population-scale phenotypes in A. thaliana, while the AraGWAS Catalog (https://aragwas.1001genomes.org) provides a publicly available, manually curated and standardized collection of marker-trait associations for all available phenotypes from AraPheno. In this major update, we introduce the next generation of both platforms, including new data, features and tools. We included novel results on associations between knockout-mutations and all AraPheno traits. Furthermore, AraPheno has been extended to display RNA-Seq data for hundreds of accessions, providing expression information for over 28 000 genes for these accessions. All data, including the imputed genotype matrix used for GWAS, are easily downloadable via the respective databases.

Network-based hierarchical population structure analysis for large genomic data sets

Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic data sets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these data sets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community-detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We show the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrate how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic data sets.

Grafting induces flowering time and tuber formation changes in Brassica species involving FT signalling

Brassica species are widely cultivated and important biennial and annual crops. The transition from vegetative to reproductive development in Brassica species is critical in agriculture and horticulture. Grafting is a useful tool for improving agricultural production and investigating the movement of long-range signals. Here we established a hypocotyl micrografting system in B. rapa crops and successfully grafted the rootstock of turnip onto many different scion genotypes. Grafting with turnip rootstock prolonged vegetative growth, delayed flowering and improved seed yield in rapeseed. The late-flowering turnip rootstock could delay flowering of the scion of the early-flowering turnip accession. The BrrFLC1 (FLOWERING LOCUS C1 in B. rapa) transcript levels and H3K4me3 levels at the BrrFLC1 locus were up-regulated and subsequently suppressed the downstream FT (FLOWERING LOCUS T) signals in leaves of the scion to delay flowering. Vernalization treatment can efficiently promote flowering time in turnip. The non-vernalised turnip flowered early after grafting onto the rootstock of the vernalised turnip, which was accompanied by high levels of FT homologue expression in leaves of the scion. Hypocotyl excision experiments revealed that the process of tuber formation was suppressed by removing the hypocotyl tissue, which in turn repressed the expression of tuberization-related genes. Our findings suggest that the rootstock generates mobile signals that are transported from the rootstock to the scion to fine-tune FT signalling and modulate flowering time.

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 .

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog

The abundance of high-quality genotype and phenotype data for the model organism Arabidopsis thaliana enables scientists to study the genetic architecture of many complex traits at an unprecedented level of detail using genome-wide association studies (GWAS). GWAS have been a great success in A. thaliana and many SNP-trait associations have been published. With the AraGWAS Catalog (https://aragwas.1001genomes.org) we provide a publicly available, manually curated and standardized GWAS catalog for all publicly available phenotypes from the central A. thaliana phenotype repository, AraPheno. All GWAS have been recomputed on the latest imputed genotype release of the 1001 Genomes Consortium using a standardized GWAS pipeline to ensure comparability between results. The catalog includes currently 167 phenotypes and more than 222 000 SNP-trait associations with P < 10⁻⁴, of which 3887 are significantly associated using permutation-based thresholds. The AraGWAS Catalog can be accessed via a modern web-interface and provides various features to easily access, download and visualize the results and summary statistics across GWAS.

A Polygenic Genetic Architecture of Flowering Time in the Worldwide Arabidopsis thaliana Population

Here, we report an empirical study of the polygenic basis underlying the evolution of complex traits. Flowering time variation measured at 10 and 16°C in the 1,001-genomes Arabidopsis thaliana collection of natural accessions were used as a model. The polygenic architecture of flowering time was defined as the 48 loci that were significantly associated with flowering time-at 10 and/or 16°C and/or their difference-in this population. Contributions from alleles at flowering time associated loci to global and local adaptation were explored by evaluating their distribution across genetically and geographically defined subpopulations across the native range of the species. The dynamics in the genetic architecture of flowering time in response to temperature was evaluated by estimating how the effects of these loci on flowering changed with growth temperature. Overall, the genetic basis of flowering time was stable-about 2/3 of the flowering time loci had similar effects at 10°C and 16°C-but many loci were involved in gene by temperature interactions. Globally present alleles, mostly of moderate effect, contributed to the differences in flowering times between the subpopulations via subtle changes in allele frequencies. More extreme local adaptations were, on several occasions, due to regional alleles with relatively large effects, and their linkage disequilibrium-patterns suggest coevolution of functionally connected alleles within local populations. Overall, these findings provide a significant contribution to our understanding about the possible modes of global and local evolution of a complex adaptive trait in A. thaliana.

Identification of FT family genes that respond to photoperiod, temperature and genotype in relation to flowering in cassava (Manihot esculenta, Crantz)

Cassava is a starch-storing root crop that is an important source of dietary energy in tropical regions of the world. Genetic improvement of cassava by breeding is hindered by late flowering and sparse flower production in lines that are needed as parents. To advance understanding of regulatory mechanisms in cassava, this work sought to identify and characterize homologs of the FLOWERING LOCUS T (FT) gene. Ten members of the phosphatidylethanolamine-binding protein gene family, to which FT belongs, were obtained from the cassava genome database. Phylogenetic and sequence analysis of these proteins was used to identify two putative FT homologs which had amino acid sequences at key positions in accordance with those predicted for functional FTs. Expression of these ten genes was determined in mature leaves, immature leaves, flower buds, fibrous roots, storage roots and stem. The FT transcripts were expressed in mature leaves, as expected for their possible role in leaf-to-apical meristem signaling. In growth chamber studies, plants flowered earlier in long-day photoperiod than in short-day photoperiod. Expression studies indicated that while MeFT1 was expressed in leaves without a clear-cut photoperiod response, MeFT2 was expressed in a photoperiod-dependent manner, consistent with its involvement in photoperiodic control of flowering. In growth chambers that subjected plants to a range of temperatures from 22 to 34 °C, flowering was delayed by warmer temperatures although MeFT1 and MeFT2 expression declined in only one genotype, indicating other factors regulate this response. The earliest flowering genotype, IBA980002, had high levels of MeFT1 and MeFT2 expression, suggesting that both homologs contribute to earliness of this genotype.