Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains

Julien Frouin; Axel Labeyrie; Arnaud Boisnard; Gian Attilio Sacchi; Nourollah Ahmadi

doi:10.1371/journal.pone.0217516

Abstract

The high concentration of arsenic (As) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL-As) and in the dehulled grain (CG-As), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL-As and CG-As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL-As and 0.48 for CG-As, ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.

Citation: Frouin J, Labeyrie A, Boisnard A, Sacchi GA, Ahmadi N (2019) Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains. PLoS ONE 14(6): e0217516. https://doi.org/10.1371/journal.pone.0217516

Editor: Jauhar Ali, International Rice Research Institute, PHILIPPINES

Received: February 7, 2019; Accepted: May 13, 2019; Published: June 13, 2019

Copyright: © 2019 Frouin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was funded by FranceAgrimer, Grants SIVAL n°2013-1296, SIVAL n° 2014-1382, and SIVAL n° 2015-0761 to NA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Presence of high concentration of arsenic (As) in the paddy fields or in the irrigation water (>20 mg Kg^-1 and 0.10 mg Kg^-1, respectively) has been reported in more than 70 countries in Asia, America and Europe [1–3]. The problem, often of geological origin, affects several hundred million peoples living in the area, especially in Asia [1, 3, 4] as it translates into rice grain. Local and regional surveys have revealed tight correlation between As concentration in the cultivated soil, or in the irrigation water, and in the rice plant [2, 5]. As accumulation in the rice plant is the highest in the roots, followed by the straw, the whole (cargo) grain and the polished (white) grain [2, 5, 6]. Presence of As in the paddy field also affects crop growth and development and, consequently, crop yield [6].

Alternate wetting and drying of the paddy field during the cropping season is the most effective way to achieve agronomic mitigation. In aerobic soils, the oxidative immobilization of As reduces its phyto-availability and uptake in rice [7]. Application of silicon fertilizer can also reduce the concentration of As in the rice plant [8]. A second category of mitigation options relies on rice genetic improvement to reduce As uptake and/or its translocation from the vegetative organs to the edible grains. A third category of mitigation relies on rice cooking methods. Washing and/or cooking in excess water reduce rice grains’ As content [9, 10].

Mechanisms of rice plant response to soil As excess have been reported to be similar to those observed for other types of soil chemical toxicity such as iron and salt [11]. However, the exact mechanisms related to the phytotoxic effects of As, and the rice defense response against As remain poorly understood. In aerobic cultivation, the predominant form of soil As is arsenate, As(OH)₅ or As(V), and its uptake by plants involves phosphate transporters [12]. Overexposure to arsenate triggers reduced expression of genes coding for arsenate/phosphate transporters such as PHT1 [13]. At the same time, the arsenate taken up undergoes chemical reduction to a more highly toxic species, arsenite [As(III)] [14, 15]. The arsenite is then either excreted into the rhizosphere [16, 17], or transported to aboveground organs [18], and/or detoxified by complexation as phyto-chelatines and compartmentalized in the cell vacuoles [19]. Within the anaerobic conditions of paddy fields, the predominant form of As is arsenite [20]. Arsenite enters root cells through aquaporin type membrane ports [21]. Transporters involved in this process include silicon transporters Lsi1 (influx) and Lsi2 (efflux) [21, 22] and several silicon-independent pathways [23, 24].

Significant genetic diversity for As accumulation has been reported in rice, under overexposure to As in hydroponic cultivation and in field experiments [25–28]. Analysis of grain As content in 421 rice accessions grown in six sites distributed in Bangladesh, China and USA revealed from 3 to 34 fold variation in each site [29]. It also revealed that accessions belonging to the Aus genetic group, originated from the Indian subcontinent, had the highest As contents.

Using recombinant inbred lines (RIL) from bi-parental crosses, several QTLs involved in As accumulation have been mapped [25, 30, 31]. Likewise, GWAS with the phenotypic data produced in the above described multilocation trial, detected several significant associations for grain As content [29, 32]. However, none of the significant associations mapped in the vicinity (distance of less than 200 kb) of the Os02g51110 and Os03g01700 loci coding for Lsi1 and Lsi2 proteins [21, 22], that can play a central role in rice response to As overexposure. Likewise, very few significant associations detected by GWAS, colocalized with QTLs mapped in RIL populations [32]. Analysis of As-induced genome-wide modulation of transcriptomes of rice seedling roots revealed up-regulation of several hundred genes, confirming the complexity of the gene network involved in response to As overexposure [33–36]. Gene families with differential gene expression in As tolerant and As-susceptible genotypes include glutathione S-transferases, cytochrome P450s, heat shock proteins, metal-binding proteins, and a large number of transporters and transcriptions factors such as MYBs [37]. More recently, using a reverse genetics approach, [38] showed that OsHAC1;1 and OsHAC1;2 functioned as arsenate reductases and played a role in the control of As accumulation in rice. Similar crucial role in As accumulation in rice was reported for OsHAC4 [16]. Based on these findings, some authors recently advocated turning to gene-editing technology for the control of As accumulation in rice grains [7, 24].

Genomic selection (GS) has recently emerged as an alternative option to conventional marker-assisted selection targeting mapped QTLs [39–41]. By shifting the plant breeding paradigm from “breeding by design” to a “genome-wide-approach”, GS provides genomic estimated breeding values (GEBV) based on all available marker data, instead of focusing on a limited set of markers that tag putative genes or QTLs. In the last few years, successful proof of GS concept has been reported in maize [42], wheat [43], barley [44] and oats [45]. In rice, moderate to high predictive ability (PA) of GEBV has been reported for a variety of quantitative traits in experiments with the progeny of bi-parental crosses and in diverse germplasm collections [46–51]. In particular, it was shown that rice diversity panels provide accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel [52]. It was also shown that genomic prediction accounting for genotype by environment interaction offered an effective framework for breeding simultaneously for adaptation to an abiotic stress and for performance under normal cropping conditions in rice [53].

The above reported findings suggest that As uptake and accumulation in rice grains are a complex process involving a large number of QTLs. We thus, hypthesized that genomic prediction offers the most effective marker assisted breeding approach for ability to prevent As accumulation in rice grains. To test this hypothesis the potential of two marker assisted selection approaches to improve the ability of rice to restrict As accumulation in the grains was evaluated. First, field phenotypic data (leaf and grain As content of rice plants grown in paddy fields with rather high As concentrations) and genotypic data from a reference diversity panel, were used to either map QTLs involved in As accumulation through GWAS or to train genomic prediction models. Second, using similar phenotypic and genotypic data from a panel of advanced lines from a breeding program, congruence between GWAS results in the two populations was analyzed, and the predictive ability of genomic prediction across the two populations was evaluated. The results identified genomic prediction as the most promising approach to improve the ability of rice to restrict As uptake and its accumulation in the grains.

Methods

Plant material

The plant material phenotyped comprised a diversity panel of 300 accessions and a set of 100 advanced inbred lines (F5, F6 or F7), all belonging to the japonica subspecies of O. sativa. The diversity panel, hereafter referred to as the reference population (RP), was composed of 214 accessions representing the European Rice Core Collection [54], and 86 accessions of direct interest for the Camargue-France breeding program (S1 Table). The 100 advanced breeding lines hereafter referred to as the validation population (VP), was composed of elite lines of the rice breeding program run by the Centre Français du Riz (CFR) and Cirad, in the Camargue region, France. Within this plant material, 228 accessions of the RP and 95 lines of the VP were genotyped and served for GWAS and genomic prediction studies.

Field trials and phenotyping

Field trials were conducted at the CFR experimental station, Mas d’Adrien (43°42’13.77”N; 4°33’44.71”E; 3 m asl.), under a standard irrigated rice cropping system. The RP was phenotyped in two consecutive years (2014 and 2015), the VP only in 2016. In 2014, the 300 accessions of RP were phenotyped under an augmented randomized complete block design repeated twice, each block being composed of 25 tested accessions and two check varieties (Albaron and Brio). To confirm the results of the 2014 trial, in 2015, 50 accessions of RP, with contrasted As content performances in 2014, were phenotyped in a complete randomized blocks design with eight replicates. In both 2014 and 2015 trials, the size of the individual plot was one row of 15 plants. In 2016, each of the 100 advanced lines of VP was represented by five full-sib lines and the size of the individual plot for each full-sib line was one row of 15 plants.

In each field trial, the concentration of total As in the flag leaf (FL-As) and in the cargo grain (CG-As) was measured and the CG-As/FL-As ratio (Ratio) calculated. In the 2014 and 2015, three biological samples were prepared for each individual plot to measure FL-As. Each biological sample was composed of three flag leaves of three different plants. Each biological sample was oven-dried at 75°C for 120 h, ground, mineralized, and total As concentration was measured using the inductively coupled plasma mass spectrum (ICP-MA; Bruker Aurora ICP Mass Spectrometer). For each biological sample, total As was measured in at least two technical samples and averaged to establish the sample phenotype. Data from the three biological samples were averaged to establish the plot phenotype. A similar procedure was applied to CG-As measurement in which the biological samples were composed of three panicles. These panicles were threshed after oven drying, the paddy grains were de-husked, and the resulting cargo grains were ground before undergoing the mineralization procedure.

In 2016, FL-As and CG-As were measured in one randomly chosen sib-line in each advanced line. Two biological samples were prepared from each chosen sib-line: one biological sample from an individual plant that was also used for DNA extraction and genotyping (see below), and a second sample from the bulk of at least three plants.

In each field trial, the soil total As content was measured before sowing and after harvest. Likewise, in each field trial, total As content of irrigation water was monitored once a month during the rice cropping cycle. Soil samples were collected from 10–15 cm depth in 1 m² area, in three points of the field. Water samples were collected monthly from the irrigation canal at its junction point with the experimental field. The total As concentration was measured using the ICP-AES (Inductively coupled plasma atomic emission spectrometry) method.

Genotypic data

Genotypic data were produced by two distinct genotyping by sequencing (GBS) experiments, for 228 accessions of RP and 95 lines of VP (because of limited funds available). In both cases, DNA libraries were prepared at the Regional Genotyping Technology Platform (http://www.gptr-lr-genotypage.com) hosted at Cirad, Montpellier, France. For each accession, genomic DNA was extracted from the leaf tissues of a single plant, using the MATAB method [55]. Each DNA sample was diluted to 100 ng/μl and digested separately with the restriction enzyme ApekI. DNA libraries were then single-end sequenced in a single-flow cell channel (i.e. 96-plex sequencing) using an Illumina HiSeq2000 (Illumina, Inc.) at the Regional Genotyping Platform (http://get.genotoul.fr/) hosted at INRA, Toulouse, France. The fastq sequences were aligned to the rice reference genome, Os-Nipponbare-Reference-IRGSP-1.0 [56] with Bowtie2 (default parameters). Non-aligning sequences and sequences with multiple positions were discarded. Single nucleotide polymorphism (SNP) calling was performed using the Tassel GBS pipeline v5.2.29 [57]. The initial filters applied were the quality score (>20), call rate >80% the count of minor alleles (>1), and the bi-allelic status of SNPs. In the second step, loci with minor allele frequency (MAF) below 2.5% and with more than 20% missing data were discarded. The missing data were imputed using Beagle v4.0 [58].

Analysis of phenotypic data

In 2014, plot level phenotypic data of the 300 accessions of RP were modeled for each trait as: where Y_ijk is the observed phenotype of accession i in replicate j and bloc k, μ is the overall mean, a_i the accession effect, r_j the replicate effect, b_jk the check effect considered as quantitative covariate, β(r)_jk the block effect within the replicate, (ar)_ij the interaction between accessions and replicates, and e_ij the residual.

In 2015, plot level phenotypic data of the 50 accessions of RP were modeled for each trait as Y_ij = μ+a_i+r_j+(ar)_ij+e_ij where Y_ij is the observed phenotype of accession i in bloc j, μ is the overall mean, a_i the accession effect, r_i the replicate effect, (ar)_ij the interaction between accession i and replicate j, considered as random, and e_ij the residual. For each dataset and each trait, least square means were estimated using the mixed model procedure of Minitab 18.1.0 statistical software.

Broad-sense heritability was calculated for each trait as: , where and are the estimates of genetic and residual variances, respectively, derived from the expected mean squares of the analysis of variance and n is the number of replicates. The computed CG-As/FL-As ratio were subjected to the angular transformation 2Arcsin square root before analysis.

Genotypic characterization of RP and VP

The genetic structure of 228 accessions of RP and 95 advanced lines of VP was analyzed jointly using a distance-based method [59]. First, a matrix of 3,620 SNPs was extracted from the working genotypic dataset of 22,370 SNPs common to RP and VP, by discarding loci that had imputed data and by imposing a minimum distance of 25 kb between two adjacent loci. Then, an unweighted neighbor-joining tree based on dissymmetry matrix was constructed using DarWin v6 software [59].

The speed of decay of linkage disequilibrium (LD) in RP and VP was estimated by computing r² between pairs of markers on a chromosome basis using Tassel 5.2 software [60], and then averaging the results by distance classes using XLStat.7.

Association analysis

Separate association analyses were performed with phenotypic and genotypic data from 228 accessions of RP and from 95 advanced lines of VP. A single marker regression-based association analysis was performed for each phenotypic trait under a mixed linear model (MLM), in which marker and population structure (Q matrix) effects were considered as fixed and the kinship effect (K matrix) was considered as random. The MLM was run under the exact method option of Tassel 5.2 software [60], where the additive genetic and residual variance components are re-estimated for each SNP. For each SNP tested, Tassel 5.2 computed a p-value, the log likelihood of the null and alternative models, and the fixed-effect weight of the SNP with its standard error. The threshold to declare the association of a SNP marker with a trait to be significant was set at a probability level of 1e-05. Genes underlying the significant loci were analyzed using the MSU database (http://rice.plantbiology.msu.edu/) search and gene annotation.

Genomic prediction

Construction of the incidence matrix.

In order to reduce possible negative effects of redundancy of marker information on the predictive ability of genomic predictions and to reduce computing time, redundant SNPs were discarded as follows. First, using the genotypic dataset of the RP (N = 228 entries and P = 22,370 SNPs), for each SNP pairwise LD with all other SNPs was calculated. Second, among each group of SNPs in complete LD (r² = 1), the first SNP along the chromosome was maintained and all the others were discarded. This procedure reduced the total number of SNP loci to 16,902. Once the list of these SNPs was established, the incidence matrix of 16,902 SNP was constructed for the VP accordingly.

Cross-validation experiment in the RP.

Three whole-genome regression models, the genomic best linear unbiased prediction (GBLUP), the Bayesian regression BayesA, and the reproducing kernel Hilbert spaces regressions (RKHS) were used to regress the FL-As, CG-As and Ratio traits of RP accessions on their genotype at 16,902 SNP loci. GBLUP is a parametric model (i.e. genetic values are approximated by using linear regression procedures) with Gaussian prior density of marker effects, that hypothesizes homogenous marker effects and strictly additive determinism of the genetic effects [61]. It was implemented using the genomic matrix G = M*M’ (M being the incidence matrix). BayesA is a parametric model with a scaled-t prior density of marker effects, i.e. density with higher mass at the neighborhood of zero, inducing strong shrinkage toward zero of effects of markers with small effects [62]. It hypothesizes that some markers are linked to QTL while others are in regions that do not harbor QTL. RKHS is a semiparametric regression model that combines the additive genetic model with a kernel function and has the ability to capture more complex genetic determinisms [63]. The three models were implemented using the BGLR statistical package [64]. The default parameters for prior specification were used and the number of iterations for the Markov chain Monte Carlo (MCMC) algorithm was set to 25,000 with a burn-in period of 5,000.

The cross-validation experiments used 171 (3/4) of the 228 accessions of the RP as the training set and the remaining 57 (1/4) accessions as the validation set. Each cross-validation experiment was repeated 100 times using 100 independent partitioning of the RP into training set and validation set. For each independent partitioning, the correlation between the predicted and the observed phenotype was calculated so as to obtain 100 correlations for each cross-validation experiment. The predictive ability of each cross-validation experiment was computed as the mean value of the 100 correlations.

To analyze sources of variation in the predictive ability of genomic predictions, the correlation (r) of repetition was transformed into a Z statistic using the equation: Z = 0.5 {ln[1+r]−ln[1−r]} and analyzed as a dependent variable in an analysis of variance. After estimation of confidence limits and means for Z, these were transformed back to r variables.

Genomic prediction across populations.

The predictive ability of genomic prediction across populations was evaluated under three scenarios of composition of the training set. Under the first scenario (S1), the training set included all 228 accessions of RP. Under S2, the training set was composed of 100 accessions of the RP with the lowest average pairwise Euclidian distances with the 95 lines of the VP. Under S3, 100 accessions of the training set were selected among the 228 accessions of RP, using the CDmean method of optimization of the training set [65]. In this 3rd scenario, a dedicated training set was selected for each phenotypic trait to account for trait heritability. Predictions were performed with the three above-described statistical methods (GBLUP, BayesA and RKHS) using the BGLR statistical package [64]. For each trait (FL-As, CG-As, and CG-As/FL-As Ratio), the predictive ability of the prediction experiment was calculated as the correlation between the predicted and the observed phenotypes of the 95 lines.

Results

Phenotypic diversity for arsenic content

In 2014, soil analyses before crop establishment and after crop harvest revealed similar As concentrations of about 10 mg kg^-1 dry soil weight. During the same period, the monthly survey of the irrigation water revealed variable As contents (0.014 to 0.034 mg l^-1) with an average of 0.021 mg l^-1. Similar soil and water As contents were observed in 2015 and 2016 (S2 Table).

Variation in arsenic content in the reference population.

The distributions of the three traits (FL-As, CG-As and Ratio) measured in 2014 among the 300 accessions of RP are presented in Fig 1. Variation in FL-As ranged from 1.393 (Bexibell accession) to 15.509 mg kg^-1 (Auzgusta accessions) and averaged 5.881 mg kg^-1 of dry weight. Variation in CG-As ranged from 0.147 (Suweon accession) to 0.656 mg kg^-1 (Octet accession) and averaged 0.335 mg kg^-1 (S1 Table). Partitioning of the observed phenotypic variations into different sources of variation via the mixed model analysis revealed a highly significant effect of accession for the three traits considered (Table 1). The model R² was greater than 0.701 for the three traits, indicating a good fit of the model. Broad-sense heritability tended to confirm this trend, with values ranging from 0.803 to 0.861 in 2014 (Table 1). The determination coefficient between FL-As and CG-As was rather low but highly significant (R² = 0.204, p < 0.0001). This rather loose relationship between FL-As and CG-As corroborates the significant accession effect observed for the Ratio trait.

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Fig 1. Distribution of adjusted phenotypic values for flag leaf arsenic content (FL-As), Cargo grain arsenic content (CG-As), and the CG-As/FL-As ratio, in the reference (RP) and validation (VP) populations.

a: data from 2014 field experiment. The vertical blue line indicates the mean value of arsenic content the in the population.

https://doi.org/10.1371/journal.pone.0217516.g001

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Table 1. Variance components of three phenotypic traits in the reference population (RP) evaluated in 2014 and in 50 selected accessions of RP evaluated in 2015.

https://doi.org/10.1371/journal.pone.0217516.t001

In 2015, the range of variation in FL-As among the 50 accessions of RP with contrasted As contents in 2014 was much larger (from 3.692 to 34.689; average of 16.833 mg kg^-1), while the range of variation in CG-As was slightly narrower (0.169 to 0.493; average of 0.338 mg kg^-1). However, these differences in the range of variation did not change the relative ranking of the 50 accessions observed in 2014. Indeed, the Spearman coefficient of rank correlation between performances of the 50 RP accessions in 2014 and 2015 was r = 0.724 (p < 0.0001) for FL-As, r = 0.681 (p < 0.0001) for CG-As, and r = 0.587 (p < 0.0001) for Ratio. The determination coefficient between FL-As and CG-As of the 50 accessions in 2015 was higher (R² = 0.563, p < 0.0001) than the one observed in 2014 for the 300 accessions.

Variation in arsenic content in the validation population.

Variation in FL-As among the 95 accessions in the VP ranged from 3.241 to 37.760 mg kg^-1 and averaged 14.651 mg kg^-1 (Fig 1). Variation in CG-As ranged from 0.208 to 0.729 mg kg^-1 and averaged 0.341 mg kg^-1. The determination coefficient between the FL-As and CG-As was low but highly significant (R² = 0.20, p < 0.0001). The computed Ratio varied between 0.179 and 0.636 and averaged 0.336.

Genetic structure of the reference and validation populations

The RP and VP genotyping experiment yielded 39,497 and 67,658 SNP loci, respectively, among which 22,370 were common to the two populations. This working dataset is presented in S3 Table. The 22,370 SNP markers of the working dataset were unevenly distributed along the 12 chromosomes (S1 Fig; S4 Table). Average marker density was one SNP every 17.1 kb. However, it ranged from one SNP every 10.7 kb in chromosome 11 to 26.7 kb in chromosome 9. The number of pairs of loci with distance greater than 250 kb, 500 kb and 1,000 kb were 175, 27 and 1, respectively.

The decay of LD over physical distance in the two populations is presented in Fig 2. For between-marker distances of 0 to 25 kb, the average r² was 0.671 and 0.733 in RP and VP, respectively. In the RP, the r² value dropped to half its initial level at around 450 kb, reached 0.2 at 1.25 Mb, and 0.1 at 2.10 Mb. In the VP, r² reached the 0.2 threshold only at pairwise distances of around 1.70 Mb, and the 0.1 threshold at distances above 3,0 Mb. No major difference in LD decay was observed between the 12 chromosomes. Given these extents of average LDs, marker density and distribution along the chromosome should not be a major limiting factor for the next steps of the study, i.e. detection of significant associations and genomic prediction. Both RP and VP populations showed similar MAF patterns for the 22,370 common SNP loci. RP and VP had the same minor allele in 95.4% of the common loci. In both populations, the MAF distribution was slightly skewed toward low frequencies, the average MAF was close to 22.2%, and the proportion of loci with MAF < 10% was close to 75%. Likewise, the Spearman correlation between the MAF of the 21,343 loci with identical minor alleles in the two populations was r = 0.849 (p <0.011).

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Fig 2. Patterns of decay in linkage disequilibrium in the reference population (RP) and in the validation population (VP).

The curves represents the average r² among the 12 chromosomes; the bars represent the associated standard deviation.

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Dissymmetry-based clustering of RP accessions led to two major clusters corresponding to the temperate japonica (65% of accessions) and tropical japonica (35% of accessions) sub-groups (Fig 3). The majority of the temperate japonica accessions are of European origin, while the majority of the tropical japonica accessions originate from the American continent. The inclusion of the VP lines in the dissymmetry-based clustering did not modify the clustering into two groups. Indeed, 69% of VP lines clustered with the temperate japonica group and the remaining 31% with the tropical japonica group (Fig 3; S1 Table).

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Fig 3. Unweighted neighbor-joining tree based on simple matching distances constructed from the genotype of 228 accessions of the reference population (RP) and 95 advanced lines of the validation population (VP), using 3,620 SNP markers.

Green: VP; Red and blue: RP accessions belonging to tropical japonica and temperate japonica, respectively.

https://doi.org/10.1371/journal.pone.0217516.g003

Relationship between genotypic and phenotypic diversity

Highly significant differences in FL-As and CG-As contents were observed between the temperate japonica and the tropical japonica accessions of RP (total of 300 accessions) evaluated in 2014. The former subgroup had the highest As contents (S5 Table; S2 Fig). Data from the 50 RP selected accessions evaluated in 2015 and from the 95 advanced lines of VP confirmed this trend.

Association analyses

Association analysis in the reference population.

Results of association analysis for FL-As, CG-As and Ratio traits in the RP are presented in Fig 4 and in S6 Table. The number of significant associations (p-value < 1e-05) was 41 for FL-As, 23 for CG-As and 82 for Ratio. These associations represented 6, 13 and 19 independent loci, i.e. a cluster of SNPs with a distance of less than 1.25 Mb between two consecutive significant SNPs, corresponding to the average LD of r² < 0.2. These loci were composed of 1–35 SNPs, not always adjacent. None of the significant SNPs or independent loci detected for one trait was found to be significant for another trait. The MAF of the significant SNPs ranged from 2.5% to 49.4% and averaged 36.1% for FL-As, 11.7% for CG-As and 27.5% for Ratio. The contribution of individual significant SNPs to the total variance of the trait considered (marker R2) was low and did not exceed 12%. Among the 41 SNPs significantly associated with FL-As, 11 corresponding to three independent loci had marker R2 > 10%. The highest marker R2 observed among the 23 SNPs significantly associated with CG-As, was 8%. Among the 82 SNPs significantly associated with Ratio, nine corresponding to six independent loci had marker R2 > 10%.

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Fig 4. Results of association analyses in the reference population (RP) and in the validation population (VP) in the present study, and comparison with data from the literature.

For the present study, data points represent SNPs significantly associated with arsenic concentration in the flag leaf (FL-As) in the cargo grain (CG-As), and the CG-As/FL-As ratio in RP and VP. Data from the literature include significant SNPs mapped by GWAS [30], QTLs for grain arsenic concentration [25, 29] and candidate genes [21, 39].

https://doi.org/10.1371/journal.pone.0217516.g004

Association analysis in the validation population.

Results of association analysis for the three traits in the VP are presented in Fig 4 and S6 Table. The number of significant associations was 15 for FL-As, 75 for CG-As and 8 for Ratio. These associations represented 8, 30 and 5 independent loci. These loci were composed of 1–22 not always adjacent SNPs, with p-values ranging between 1e-05 and 1e-09. Similar to what was observed in association analysis in the RP, in the VP significant SNP loci for the three traits did not colocalize. The MAF of the significant SNP ranged from 2.6% to 46.8% and averaged 28.0% for FL-As, 9.0% for CG-As and 9.1% for Ratio. The significant SNPs contributed much more, on average, to trait total variance than the ones observed in the RP. The mean marker R2 was 18% for SNPs associated with FL-As, 24% for SNPs associated with CG-As and 16% for SNPs associated with Ratio.

Congruence between the results of GWAS in RP and in VP.

Among the 146 SNPs significantly associated with one of the three traits in the RP, only eight were also significant in the VP. These SNPs corresponded to one independent locus associated with CG-As. The application of a margin of tolerance of 1.7 Mb between a significant locus in the RP and its counterpart in the VP (corresponding to the average distances for LD of 0.2 in the VP) increased slightly the number of colocalizations: four additional colocalizations for CG-As and one for Ratio. The number of such colocalizations increased markedly (9, 20 and 12 for FL-As, CG-As and Ratio, respectively) when the threshold of significance of association in the two populations was lowered to a p-value < 1e-04 (Fig 4). The latter features represented 69%, 40% and 52% of the independent significant loci detected in RP for FL-As, CG-As and Ratio, respectively.

Genomic localization and co-localization with QTLs and gene reported in the literature.

Out of a total of 146 SNPs significantly associated with one of the three As related traits in the RP, 41% were located in intergenic regions, 14% in introns, 27% in exons with synonymous coding effects, 10% in exons with non-synonymous coding effects, 6% in UTR-3 regions and 2% in stop-gained sites (S7 Table). The proportions were similar for the 96 significant loci in the VP and for those observed among all the 22,370 SNPs used for GWAS. Genes underlying the significant loci included ATP binding cassette involved in As detoxification (e.g. Os04g0620000), transporters (e.g. phosphate, ammonium, peptide, efflux transporters MATE), abiotic stress responsive genes (e.g. several F-box and DUF domain containing proteins, cytochrome P450), and transcription factors (e.g. MBY, zinc finger family protein, ERF).

A genome survey within an interval of 400 kb (200 kb downstream and 200 kb upstream) surrounding each significant SNP in the RP and in the VP, led to the identification of at least one gene with the product involved in plant response to abiotic stresses or reported in the literature as responsive to As stress (Fig 4 and S7 Table). The latter included OsLsi1, OsHAC1, OsHAC6, OsACR2-1 and representative of glutathione S-transferases, Cytochrome P450s, heat shock proteins, metal-binding proteins, phosphate acquisition proteins, transporter proteins and transcription factors. Likewise, survey of the surrounding interval of 400 kb of the significant SNPs for QTL reported in the literature to be associated with As resulted in a large number of colocalizations (Fig 4 and S7 Table).